id	sid	tid	token	lemma	pos
ijassa-497	1	1	adv	adv	PROPN
ijassa-497	1	2	syst	syst	PROPN
ijassa-497	1	3	sci	sci	PROPN
ijassa-497	1	4	appl	appl	PROPN
ijassa-497	1	5	2017	2017	NUM
ijassa-497	1	6	;	;	PUNCT
ijassa-497	1	7	3:22–33	3:22–33	NUM
ijassa-497	1	8	published	publish	VERB
ijassa-497	1	9	online	online	ADV
ijassa-497	1	10	at	at	ADP
ijassa-497	1	11	http://ijassa.ipu.ru/ojs/ijassa/article/view/497	http://ijassa.ipu.ru/ojs/ijassa/article/view/497	PROPN
ijassa-497	1	12	conformal	conformal	ADJ
ijassa-497	1	13	kernel	kernel	PROPN
ijassa-497	1	14	expected	expect	VERB
ijassa-497	1	15	similarity	similarity	NOUN
ijassa-497	1	16	for	for	ADP
ijassa-497	1	17	anomaly	anomaly	NOUN
ijassa-497	1	18	detection	detection	NOUN
ijassa-497	1	19	in	in	ADP
ijassa-497	1	20	time	time	NOUN
ijassa-497	1	21	-	-	PUNCT
ijassa-497	1	22	series	series	NOUN
ijassa-497	1	23	data	data	PROPN
ijassa-497	1	24	aleksandr	aleksandr	PROPN
ijassa-497	1	25	safin1,2	safin1,2	PROPN
ijassa-497	1	26	,	,	PUNCT
ijassa-497	1	27	evgeny	evgeny	PROPN
ijassa-497	1	28	burnaev2,3	burnaev2,3	PROPN
ijassa-497	1	29	*	*	PROPN
ijassa-497	1	30	1	1	NUM
ijassa-497	1	31	national	national	PROPN
ijassa-497	1	32	research	research	PROPN
ijassa-497	1	33	university	university	PROPN
ijassa-497	1	34	higher	high	ADJ
ijassa-497	1	35	school	school	NOUN
ijassa-497	1	36	of	of	ADP
ijassa-497	1	37	economics	economic	NOUN
ijassa-497	1	38	,	,	PUNCT
ijassa-497	1	39	moscow	moscow	PROPN
ijassa-497	1	40	,	,	PUNCT
ijassa-497	1	41	russia	russia	PROPN
ijassa-497	1	42	2	2	NUM
ijassa-497	1	43	skolkovo	skolkovo	PROPN
ijassa-497	1	44	institute	institute	PROPN
ijassa-497	1	45	of	of	ADP
ijassa-497	1	46	science	science	NOUN
ijassa-497	1	47	and	and	CCONJ
ijassa-497	1	48	technology	technology	NOUN
ijassa-497	1	49	,	,	PUNCT
ijassa-497	1	50	skolkovo	skolkovo	PROPN
ijassa-497	1	51	,	,	PUNCT
ijassa-497	1	52	moscow	moscow	PROPN
ijassa-497	1	53	region	region	NOUN
ijassa-497	1	54	,	,	PUNCT
ijassa-497	1	55	russia	russia	PROPN
ijassa-497	1	56	3	3	NUM
ijassa-497	1	57	institute	institute	PROPN
ijassa-497	1	58	for	for	ADP
ijassa-497	1	59	information	information	NOUN
ijassa-497	1	60	transmission	transmission	NOUN
ijassa-497	1	61	problems	problem	NOUN
ijassa-497	1	62	,	,	PUNCT
ijassa-497	1	63	moscow	moscow	PROPN
ijassa-497	1	64	,	,	PUNCT
ijassa-497	1	65	russia	russia	PROPN
ijassa-497	1	66	abstract	abstract	NOUN
ijassa-497	1	67	:	:	PUNCT
ijassa-497	1	68	the	the	DET
ijassa-497	1	69	problem	problem	NOUN
ijassa-497	1	70	of	of	ADP
ijassa-497	1	71	anomaly	anomaly	NOUN
ijassa-497	1	72	detection	detection	NOUN
ijassa-497	1	73	arises	arise	VERB
ijassa-497	1	74	in	in	ADP
ijassa-497	1	75	many	many	ADJ
ijassa-497	1	76	practical	practical	ADJ
ijassa-497	1	77	applications	application	NOUN
ijassa-497	1	78	.	.	PUNCT
ijassa-497	2	1	currently	currently	ADV
ijassa-497	2	2	it	it	PRON
ijassa-497	2	3	is	be	AUX
ijassa-497	2	4	highly	highly	ADV
ijassa-497	2	5	important	important	ADJ
ijassa-497	2	6	to	to	PART
ijassa-497	2	7	be	be	AUX
ijassa-497	2	8	able	able	ADJ
ijassa-497	2	9	to	to	PART
ijassa-497	2	10	detect	detect	VERB
ijassa-497	2	11	outliers	outlier	NOUN
ijassa-497	2	12	in	in	ADP
ijassa-497	2	13	data	datum	NOUN
ijassa-497	2	14	streams	stream	NOUN
ijassa-497	2	15	,	,	PUNCT
ijassa-497	2	16	as	as	SCONJ
ijassa-497	2	17	recent	recent	ADJ
ijassa-497	2	18	years	year	NOUN
ijassa-497	2	19	have	have	AUX
ijassa-497	2	20	seen	see	VERB
ijassa-497	2	21	a	a	DET
ijassa-497	2	22	rapid	rapid	ADJ
ijassa-497	2	23	growth	growth	NOUN
ijassa-497	2	24	in	in	ADP
ijassa-497	2	25	the	the	DET
ijassa-497	2	26	amount	amount	NOUN
ijassa-497	2	27	of	of	ADP
ijassa-497	2	28	such	such	ADJ
ijassa-497	2	29	data	datum	NOUN
ijassa-497	2	30	.	.	PUNCT
ijassa-497	3	1	only	only	ADV
ijassa-497	3	2	a	a	DET
ijassa-497	3	3	few	few	ADJ
ijassa-497	3	4	techniques	technique	NOUN
ijassa-497	3	5	are	be	AUX
ijassa-497	3	6	applicable	applicable	ADJ
ijassa-497	3	7	to	to	ADP
ijassa-497	3	8	real	real	ADJ
ijassa-497	3	9	-	-	PUNCT
ijassa-497	3	10	time	time	NOUN
ijassa-497	3	11	data	datum	NOUN
ijassa-497	3	12	and	and	CCONJ
ijassa-497	3	13	even	even	ADV
ijassa-497	3	14	fewer	few	ADJ
ijassa-497	3	15	could	could	AUX
ijassa-497	3	16	provide	provide	VERB
ijassa-497	3	17	an	an	DET
ijassa-497	3	18	interpretable	interpretable	ADJ
ijassa-497	3	19	anomaly	anomaly	NOUN
ijassa-497	3	20	score	score	NOUN
ijassa-497	3	21	.	.	PUNCT
ijassa-497	4	1	probabilistic	probabilistic	ADJ
ijassa-497	4	2	interpretation	interpretation	NOUN
ijassa-497	4	3	of	of	ADP
ijassa-497	4	4	the	the	DET
ijassa-497	4	5	anomaly	anomaly	NOUN
ijassa-497	4	6	score	score	NOUN
ijassa-497	4	7	could	could	AUX
ijassa-497	4	8	allow	allow	VERB
ijassa-497	4	9	an	an	DET
ijassa-497	4	10	analyst	analyst	NOUN
ijassa-497	4	11	to	to	PART
ijassa-497	4	12	choose	choose	VERB
ijassa-497	4	13	the	the	DET
ijassa-497	4	14	anomaly	anomaly	NOUN
ijassa-497	4	15	threshold	threshold	NOUN
ijassa-497	4	16	based	base	VERB
ijassa-497	4	17	on	on	ADP
ijassa-497	4	18	the	the	DET
ijassa-497	4	19	desired	desire	VERB
ijassa-497	4	20	false	false	ADJ
ijassa-497	4	21	alarm	alarm	NOUN
ijassa-497	4	22	rate	rate	NOUN
ijassa-497	4	23	,	,	PUNCT
ijassa-497	4	24	which	which	PRON
ijassa-497	4	25	is	be	AUX
ijassa-497	4	26	highly	highly	ADV
ijassa-497	4	27	important	important	ADJ
ijassa-497	4	28	in	in	ADP
ijassa-497	4	29	a	a	DET
ijassa-497	4	30	number	number	NOUN
ijassa-497	4	31	of	of	ADP
ijassa-497	4	32	real	real	ADJ
ijassa-497	4	33	-	-	PUNCT
ijassa-497	4	34	life	life	NOUN
ijassa-497	4	35	applications	application	NOUN
ijassa-497	4	36	.	.	PUNCT
ijassa-497	5	1	we	we	PRON
ijassa-497	5	2	propose	propose	VERB
ijassa-497	5	3	a	a	DET
ijassa-497	5	4	modification	modification	NOUN
ijassa-497	5	5	of	of	ADP
ijassa-497	5	6	the	the	DET
ijassa-497	5	7	expose	expose	ADJ
ijassa-497	5	8	algorithm	algorithm	NOUN
ijassa-497	5	9	for	for	ADP
ijassa-497	5	10	anomaly	anomaly	NOUN
ijassa-497	5	11	detection	detection	NOUN
ijassa-497	5	12	in	in	ADP
ijassa-497	5	13	time	time	NOUN
ijassa-497	5	14	series	series	PROPN
ijassa-497	5	15	data	data	PROPN
ijassa-497	5	16	,	,	PUNCT
ijassa-497	5	17	which	which	PRON
ijassa-497	5	18	produces	produce	VERB
ijassa-497	5	19	a	a	DET
ijassa-497	5	20	probabilistic	probabilistic	ADJ
ijassa-497	5	21	score	score	NOUN
ijassa-497	5	22	of	of	ADP
ijassa-497	5	23	abnormality	abnormality	NOUN
ijassa-497	5	24	.	.	PUNCT
ijassa-497	6	1	the	the	DET
ijassa-497	6	2	proposed	propose	VERB
ijassa-497	6	3	algorithm	algorithm	NOUN
ijassa-497	6	4	is	be	AUX
ijassa-497	6	5	developed	develop	VERB
ijassa-497	6	6	within	within	ADP
ijassa-497	6	7	the	the	DET
ijassa-497	6	8	framework	framework	NOUN
ijassa-497	6	9	of	of	ADP
ijassa-497	6	10	conformal	conformal	ADJ
ijassa-497	6	11	anomaly	anomaly	NOUN
ijassa-497	6	12	detection	detection	NOUN
ijassa-497	6	13	and	and	CCONJ
ijassa-497	6	14	utilizes	utilize	VERB
ijassa-497	6	15	the	the	DET
ijassa-497	6	16	expected	expect	VERB
ijassa-497	6	17	similarity	similarity	NOUN
ijassa-497	6	18	as	as	ADP
ijassa-497	6	19	a	a	DET
ijassa-497	6	20	measure	measure	NOUN
ijassa-497	6	21	of	of	ADP
ijassa-497	6	22	non	non	NOUN
ijassa-497	6	23	-	-	NOUN
ijassa-497	6	24	conformity	conformity	NOUN
ijassa-497	6	25	.	.	PUNCT
ijassa-497	7	1	keywords	keyword	NOUN
ijassa-497	7	2	:	:	PUNCT
ijassa-497	7	3	anomaly	anomaly	NOUN
ijassa-497	7	4	detection	detection	NOUN
ijassa-497	7	5	,	,	PUNCT
ijassa-497	7	6	conformal	conformal	ADJ
ijassa-497	7	7	prediction	prediction	NOUN
ijassa-497	7	8	,	,	PUNCT
ijassa-497	7	9	time	time	NOUN
ijassa-497	7	10	series	series	PROPN
ijassa-497	7	11	,	,	PUNCT
ijassa-497	7	12	kernel	kernel	PROPN
ijassa-497	7	13	methods	method	NOUN
ijassa-497	7	14	,	,	PUNCT
ijassa-497	7	15	expected	expect	VERB
ijassa-497	7	16	similarity	similarity	NOUN
ijassa-497	7	17	1	1	NUM
ijassa-497	7	18	.	.	PUNCT
ijassa-497	8	1	introduction	introduction	NOUN
ijassa-497	8	2	there	there	PRON
ijassa-497	8	3	are	be	VERB
ijassa-497	8	4	many	many	ADJ
ijassa-497	8	5	cases	case	NOUN
ijassa-497	8	6	in	in	ADP
ijassa-497	8	7	which	which	PRON
ijassa-497	8	8	it	it	PRON
ijassa-497	8	9	is	be	AUX
ijassa-497	8	10	highly	highly	ADV
ijassa-497	8	11	important	important	ADJ
ijassa-497	8	12	to	to	PART
ijassa-497	8	13	determine	determine	VERB
ijassa-497	8	14	whether	whether	SCONJ
ijassa-497	8	15	a	a	DET
ijassa-497	8	16	new	new	ADJ
ijassa-497	8	17	observation	observation	NOUN
ijassa-497	8	18	comes	come	VERB
ijassa-497	8	19	from	from	ADP
ijassa-497	8	20	the	the	DET
ijassa-497	8	21	same	same	ADJ
ijassa-497	8	22	distribution	distribution	NOUN
ijassa-497	8	23	or	or	CCONJ
ijassa-497	8	24	not	not	PART
ijassa-497	8	25	.	.	PUNCT
ijassa-497	9	1	this	this	DET
ijassa-497	9	2	problem	problem	NOUN
ijassa-497	9	3	is	be	AUX
ijassa-497	9	4	referred	refer	VERB
ijassa-497	9	5	to	to	ADP
ijassa-497	9	6	as	as	ADP
ijassa-497	9	7	outlier	outlier	NOUN
ijassa-497	9	8	or	or	CCONJ
ijassa-497	9	9	anomaly	anomaly	NOUN
ijassa-497	9	10	detection	detection	NOUN
ijassa-497	9	11	.	.	PUNCT
ijassa-497	10	1	e.g.	e.g.	ADV
ijassa-497	10	2	when	when	SCONJ
ijassa-497	10	3	a	a	DET
ijassa-497	10	4	fitted	fit	VERB
ijassa-497	10	5	model	model	NOUN
ijassa-497	10	6	is	be	AUX
ijassa-497	10	7	applied	apply	VERB
ijassa-497	10	8	to	to	ADP
ijassa-497	10	9	new	new	ADJ
ijassa-497	10	10	data	datum	NOUN
ijassa-497	10	11	,	,	PUNCT
ijassa-497	10	12	it	it	PRON
ijassa-497	10	13	should	should	AUX
ijassa-497	10	14	be	be	AUX
ijassa-497	10	15	checked	check	VERB
ijassa-497	10	16	whether	whether	SCONJ
ijassa-497	10	17	a	a	DET
ijassa-497	10	18	test	test	NOUN
ijassa-497	10	19	data	datum	NOUN
ijassa-497	10	20	set	set	VERB
ijassa-497	10	21	belongs	belong	VERB
ijassa-497	10	22	to	to	ADP
ijassa-497	10	23	the	the	DET
ijassa-497	10	24	same	same	ADJ
ijassa-497	10	25	population	population	NOUN
ijassa-497	10	26	as	as	SCONJ
ijassa-497	10	27	the	the	DET
ijassa-497	10	28	training	training	NOUN
ijassa-497	10	29	data	datum	NOUN
ijassa-497	10	30	set	set	VERB
ijassa-497	10	31	.	.	PUNCT
ijassa-497	11	1	to	to	PART
ijassa-497	11	2	address	address	VERB
ijassa-497	11	3	the	the	DET
ijassa-497	11	4	issue	issue	NOUN
ijassa-497	11	5	of	of	ADP
ijassa-497	11	6	novelty	novelty	NOUN
ijassa-497	11	7	detection	detection	NOUN
ijassa-497	11	8	,	,	PUNCT
ijassa-497	11	9	anomaly	anomaly	NOUN
ijassa-497	11	10	detection	detection	NOUN
ijassa-497	11	11	techniques	technique	NOUN
ijassa-497	11	12	can	can	AUX
ijassa-497	11	13	be	be	AUX
ijassa-497	11	14	used	use	VERB
ijassa-497	11	15	.	.	PUNCT
ijassa-497	12	1	anomaly	anomaly	PROPN
ijassa-497	12	2	detection	detection	NOUN
ijassa-497	12	3	has	have	AUX
ijassa-497	12	4	proven	prove	VERB
ijassa-497	12	5	to	to	PART
ijassa-497	12	6	be	be	AUX
ijassa-497	12	7	helpful	helpful	ADJ
ijassa-497	12	8	for	for	ADP
ijassa-497	12	9	certain	certain	ADJ
ijassa-497	12	10	medical	medical	ADJ
ijassa-497	12	11	purposes	purpose	NOUN
ijassa-497	12	12	,	,	PUNCT
ijassa-497	12	13	fraud	fraud	NOUN
ijassa-497	12	14	detection	detection	NOUN
ijassa-497	12	15	and	and	CCONJ
ijassa-497	12	16	machine	machine	NOUN
ijassa-497	12	17	diagnostics	diagnostic	NOUN
ijassa-497	12	18	,	,	PUNCT
ijassa-497	12	19	to	to	PART
ijassa-497	12	20	name	name	VERB
ijassa-497	12	21	but	but	CCONJ
ijassa-497	12	22	a	a	DET
ijassa-497	12	23	few	few	ADJ
ijassa-497	12	24	.	.	PUNCT
ijassa-497	13	1	for	for	ADP
ijassa-497	13	2	instance	instance	NOUN
ijassa-497	13	3	,	,	PUNCT
ijassa-497	13	4	in	in	ADP
ijassa-497	13	5	[	[	PUNCT
ijassa-497	13	6	1	1	NUM
ijassa-497	13	7	]	]	PUNCT
ijassa-497	13	8	failure	failure	NOUN
ijassa-497	13	9	prediction	prediction	NOUN
ijassa-497	13	10	for	for	ADP
ijassa-497	13	11	aircrafts	aircraft	NOUN
ijassa-497	13	12	is	be	AUX
ijassa-497	13	13	considered	consider	VERB
ijassa-497	13	14	.	.	PUNCT
ijassa-497	14	1	the	the	DET
ijassa-497	14	2	definition	definition	NOUN
ijassa-497	14	3	of	of	ADP
ijassa-497	14	4	an	an	DET
ijassa-497	14	5	anomaly	anomaly	NOUN
ijassa-497	14	6	varies	vary	VERB
ijassa-497	14	7	between	between	ADP
ijassa-497	14	8	algorithms	algorithm	NOUN
ijassa-497	14	9	and	and	CCONJ
ijassa-497	14	10	applications	application	NOUN
ijassa-497	14	11	.	.	PUNCT
ijassa-497	15	1	in	in	ADP
ijassa-497	15	2	general	general	ADJ
ijassa-497	15	3	,	,	PUNCT
ijassa-497	15	4	an	an	DET
ijassa-497	15	5	anomaly	anomaly	NOUN
ijassa-497	15	6	“	"	PUNCT
ijassa-497	15	7	is	be	AUX
ijassa-497	15	8	an	an	DET
ijassa-497	15	9	element	element	NOUN
ijassa-497	15	10	whose	whose	DET
ijassa-497	15	11	properties	property	NOUN
ijassa-497	15	12	differ	differ	VERB
ijassa-497	15	13	from	from	ADP
ijassa-497	15	14	the	the	DET
ijassa-497	15	15	majority	majority	NOUN
ijassa-497	15	16	of	of	ADP
ijassa-497	15	17	the	the	DET
ijassa-497	15	18	other	other	ADJ
ijassa-497	15	19	elements	element	NOUN
ijassa-497	15	20	under	under	ADP
ijassa-497	15	21	consideration	consideration	NOUN
ijassa-497	15	22	which	which	PRON
ijassa-497	15	23	is	be	AUX
ijassa-497	15	24	called	call	VERB
ijassa-497	15	25	as	as	ADP
ijassa-497	15	26	normal	normal	ADJ
ijassa-497	15	27	data	datum	NOUN
ijassa-497	15	28	”	"	PUNCT
ijassa-497	15	29	[	[	X
ijassa-497	15	30	14	14	NUM
ijassa-497	15	31	]	]	PUNCT
ijassa-497	15	32	.	.	PUNCT
ijassa-497	16	1	in	in	ADP
ijassa-497	16	2	[	[	X
ijassa-497	16	3	15	15	NUM
ijassa-497	16	4	]	]	PUNCT
ijassa-497	16	5	anomaly	anomaly	NOUN
ijassa-497	16	6	detection	detection	NOUN
ijassa-497	16	7	is	be	AUX
ijassa-497	16	8	described	describe	VERB
ijassa-497	16	9	as	as	SCONJ
ijassa-497	16	10	follows	follow	VERB
ijassa-497	16	11	:	:	PUNCT
ijassa-497	16	12	“	"	PUNCT
ijassa-497	16	13	anomaly	anomaly	NOUN
ijassa-497	16	14	detection	detection	NOUN
ijassa-497	16	15	refers	refer	VERB
ijassa-497	16	16	to	to	ADP
ijassa-497	16	17	the	the	DET
ijassa-497	16	18	problem	problem	NOUN
ijassa-497	16	19	of	of	ADP
ijassa-497	16	20	finding	find	VERB
ijassa-497	16	21	patterns	pattern	NOUN
ijassa-497	16	22	in	in	ADP
ijassa-497	16	23	data	datum	NOUN
ijassa-497	16	24	that	that	PRON
ijassa-497	16	25	do	do	AUX
ijassa-497	16	26	not	not	PART
ijassa-497	16	27	conform	conform	VERB
ijassa-497	16	28	to	to	ADP
ijassa-497	16	29	expected	expect	VERB
ijassa-497	16	30	behavior	behavior	NOUN
ijassa-497	16	31	”	"	PUNCT
ijassa-497	16	32	.	.	PUNCT
ijassa-497	17	1	summing	sum	VERB
ijassa-497	17	2	up	up	ADP
ijassa-497	17	3	,	,	PUNCT
ijassa-497	17	4	the	the	DET
ijassa-497	17	5	problem	problem	NOUN
ijassa-497	17	6	of	of	ADP
ijassa-497	17	7	anomaly	anomaly	NOUN
ijassa-497	17	8	detection	detection	NOUN
ijassa-497	17	9	can	can	AUX
ijassa-497	17	10	be	be	AUX
ijassa-497	17	11	formulated	formulate	VERB
ijassa-497	17	12	as	as	SCONJ
ijassa-497	17	13	follows	follow	VERB
ijassa-497	17	14	:	:	PUNCT
ijassa-497	17	15	the	the	DET
ijassa-497	17	16	task	task	NOUN
ijassa-497	17	17	is	be	AUX
ijassa-497	17	18	to	to	PART
ijassa-497	17	19	determine	determine	VERB
ijassa-497	17	20	for	for	ADP
ijassa-497	17	21	every	every	DET
ijassa-497	17	22	object	object	NOUN
ijassa-497	17	23	in	in	ADP
ijassa-497	17	24	a	a	DET
ijassa-497	17	25	test	test	NOUN
ijassa-497	17	26	set	set	VERB
ijassa-497	17	27	whether	whether	SCONJ
ijassa-497	17	28	it	it	PRON
ijassa-497	17	29	is	be	AUX
ijassa-497	17	30	a	a	DET
ijassa-497	17	31	normal	normal	ADJ
ijassa-497	17	32	or	or	CCONJ
ijassa-497	17	33	abnormal	abnormal	ADJ
ijassa-497	17	34	instance	instance	NOUN
ijassa-497	17	35	in	in	ADP
ijassa-497	17	36	comparison	comparison	NOUN
ijassa-497	17	37	with	with	ADP
ijassa-497	17	38	observations	observation	NOUN
ijassa-497	17	39	from	from	ADP
ijassa-497	17	40	a	a	DET
ijassa-497	17	41	training	training	NOUN
ijassa-497	17	42	set	set	NOUN
ijassa-497	17	43	.	.	PUNCT
ijassa-497	18	1	anomaly	anomaly	PROPN
ijassa-497	18	2	detection	detection	NOUN
ijassa-497	18	3	approaches	approach	NOUN
ijassa-497	18	4	can	can	AUX
ijassa-497	18	5	be	be	AUX
ijassa-497	18	6	divided	divide	VERB
ijassa-497	18	7	in	in	ADP
ijassa-497	18	8	the	the	DET
ijassa-497	18	9	following	follow	VERB
ijassa-497	18	10	three	three	NUM
ijassa-497	18	11	groups	group	NOUN
ijassa-497	18	12	[	[	X
ijassa-497	18	13	15	15	NUM
ijassa-497	18	14	]	]	SYM
ijassa-497	18	15	:	:	PUNCT
ijassa-497	18	16	•	•	NUM
ijassa-497	18	17	unsupervised	unsupervised	ADJ
ijassa-497	18	18	approaches	approach	NOUN
ijassa-497	18	19	use	use	VERB
ijassa-497	18	20	only	only	ADV
ijassa-497	18	21	the	the	DET
ijassa-497	18	22	assumption	assumption	NOUN
ijassa-497	18	23	that	that	SCONJ
ijassa-497	18	24	most	most	ADJ
ijassa-497	18	25	observations	observation	NOUN
ijassa-497	18	26	are	be	AUX
ijassa-497	18	27	normal	normal	ADJ
ijassa-497	18	28	.	.	PUNCT
ijassa-497	19	1	such	such	ADJ
ijassa-497	19	2	assumption	assumption	NOUN
ijassa-497	19	3	favours	favour	VERB
ijassa-497	19	4	incremental	incremental	ADJ
ijassa-497	19	5	and	and	CCONJ
ijassa-497	19	6	autonomous	autonomous	ADJ
ijassa-497	19	7	learning	learning	NOUN
ijassa-497	19	8	in	in	ADP
ijassa-497	19	9	data	datum	NOUN
ijassa-497	19	10	streams	stream	NOUN
ijassa-497	19	11	.	.	PUNCT
ijassa-497	20	1	∗corresponding	∗corresponde	VERB
ijassa-497	20	2	author	author	NOUN
ijassa-497	20	3	:	:	PUNCT
ijassa-497	20	4	e.burnaev@skoltech.ru	e.burnaev@skoltech.ru	ADJ
ijassa-497	21	1	http://ijassa.ipu.ru/ojs/ijassa/article/view/497	http://ijassa.ipu.ru/ojs/ijassa/article/view/497	PROPN
ijassa-497	21	2	conformal	conformal	ADJ
ijassa-497	21	3	kernel	kernel	NOUN
ijassa-497	21	4	expected	expect	VERB
ijassa-497	21	5	similarity	similarity	NOUN
ijassa-497	21	6	for	for	ADP
ijassa-497	21	7	anomaly	anomaly	NOUN
ijassa-497	21	8	detection	detection	NOUN
ijassa-497	21	9	in	in	ADP
ijassa-497	21	10	time	time	NOUN
ijassa-497	21	11	-	-	PUNCT
ijassa-497	21	12	series	series	NOUN
ijassa-497	21	13	data	datum	NOUN
ijassa-497	21	14	23	23	NUM
ijassa-497	21	15	•	•	NUM
ijassa-497	21	16	supervised	supervised	ADJ
ijassa-497	21	17	approaches	approach	NOUN
ijassa-497	21	18	require	require	VERB
ijassa-497	21	19	availability	availability	NOUN
ijassa-497	21	20	of	of	ADP
ijassa-497	21	21	a	a	DET
ijassa-497	21	22	labelled	label	VERB
ijassa-497	21	23	training	training	NOUN
ijassa-497	21	24	set	set	NOUN
ijassa-497	21	25	containing	contain	VERB
ijassa-497	21	26	instances	instance	NOUN
ijassa-497	21	27	of	of	ADP
ijassa-497	21	28	both	both	CCONJ
ijassa-497	21	29	normal	normal	ADJ
ijassa-497	21	30	and	and	CCONJ
ijassa-497	21	31	abnormal	abnormal	ADJ
ijassa-497	21	32	objects	object	NOUN
ijassa-497	21	33	.	.	PUNCT
ijassa-497	22	1	•	•	NUM
ijassa-497	22	2	semi	semi	ADJ
ijassa-497	22	3	-	-	ADJ
ijassa-497	22	4	supervised	supervised	ADJ
ijassa-497	22	5	approaches	approach	NOUN
ijassa-497	22	6	require	require	VERB
ijassa-497	22	7	a	a	DET
ijassa-497	22	8	small	small	ADJ
ijassa-497	22	9	amount	amount	NOUN
ijassa-497	22	10	of	of	ADP
ijassa-497	22	11	labeled	label	VERB
ijassa-497	22	12	data	datum	NOUN
ijassa-497	22	13	with	with	ADP
ijassa-497	22	14	a	a	DET
ijassa-497	22	15	large	large	ADJ
ijassa-497	22	16	amount	amount	NOUN
ijassa-497	22	17	of	of	ADP
ijassa-497	22	18	unlabeled	unlabeled	ADJ
ijassa-497	22	19	data	datum	NOUN
ijassa-497	22	20	.	.	PUNCT
ijassa-497	23	1	in	in	ADP
ijassa-497	23	2	many	many	ADJ
ijassa-497	23	3	practical	practical	ADJ
ijassa-497	23	4	cases	case	NOUN
ijassa-497	23	5	a	a	DET
ijassa-497	23	6	number	number	NOUN
ijassa-497	23	7	of	of	ADP
ijassa-497	23	8	outliers	outlier	NOUN
ijassa-497	23	9	is	be	AUX
ijassa-497	23	10	significantly	significantly	ADV
ijassa-497	23	11	smaller	small	ADJ
ijassa-497	23	12	than	than	ADP
ijassa-497	23	13	a	a	DET
ijassa-497	23	14	number	number	NOUN
ijassa-497	23	15	of	of	ADP
ijassa-497	23	16	target	target	NOUN
ijassa-497	23	17	observations	observation	NOUN
ijassa-497	23	18	,	,	PUNCT
ijassa-497	23	19	and	and	CCONJ
ijassa-497	23	20	thus	thus	ADV
ijassa-497	23	21	usual	usual	ADJ
ijassa-497	23	22	classification	classification	NOUN
ijassa-497	23	23	methods	method	NOUN
ijassa-497	23	24	may	may	AUX
ijassa-497	23	25	yield	yield	VERB
ijassa-497	23	26	unsatisfactory	unsatisfactory	ADJ
ijassa-497	23	27	results	result	NOUN
ijassa-497	23	28	as	as	SCONJ
ijassa-497	23	29	classes	class	NOUN
ijassa-497	23	30	in	in	ADP
ijassa-497	23	31	a	a	DET
ijassa-497	23	32	dataset	dataset	NOUN
ijassa-497	23	33	are	be	AUX
ijassa-497	23	34	very	very	ADV
ijassa-497	23	35	imbalanced	imbalanced	ADJ
ijassa-497	23	36	.	.	PUNCT
ijassa-497	24	1	the	the	DET
ijassa-497	24	2	significant	significant	ADJ
ijassa-497	24	3	dominance	dominance	NOUN
ijassa-497	24	4	of	of	ADP
ijassa-497	24	5	target	target	NOUN
ijassa-497	24	6	instances	instance	NOUN
ijassa-497	24	7	over	over	ADP
ijassa-497	24	8	outliers	outlier	NOUN
ijassa-497	24	9	is	be	AUX
ijassa-497	24	10	a	a	DET
ijassa-497	24	11	natural	natural	ADJ
ijassa-497	24	12	property	property	NOUN
ijassa-497	24	13	of	of	ADP
ijassa-497	24	14	real	real	ADJ
ijassa-497	24	15	-	-	PUNCT
ijassa-497	24	16	life	life	NOUN
ijassa-497	24	17	data	datum	NOUN
ijassa-497	24	18	:	:	PUNCT
ijassa-497	24	19	e.g.	e.g.	ADV
ijassa-497	24	20	in	in	ADP
ijassa-497	24	21	case	case	NOUN
ijassa-497	24	22	of	of	ADP
ijassa-497	24	23	air	air	NOUN
ijassa-497	24	24	traffic	traffic	NOUN
ijassa-497	24	25	safety	safety	NOUN
ijassa-497	24	26	problems	problem	NOUN
ijassa-497	24	27	accidents	accident	NOUN
ijassa-497	24	28	happen	happen	VERB
ijassa-497	24	29	very	very	ADV
ijassa-497	24	30	rarely	rarely	ADV
ijassa-497	24	31	.	.	PUNCT
ijassa-497	25	1	another	another	DET
ijassa-497	25	2	reason	reason	NOUN
ijassa-497	25	3	for	for	ADP
ijassa-497	25	4	that	that	PRON
ijassa-497	25	5	is	be	AUX
ijassa-497	25	6	the	the	DET
ijassa-497	25	7	impossibility	impossibility	NOUN
ijassa-497	25	8	or	or	CCONJ
ijassa-497	25	9	very	very	ADV
ijassa-497	25	10	high	high	ADJ
ijassa-497	25	11	costs	cost	NOUN
ijassa-497	25	12	of	of	ADP
ijassa-497	25	13	reproducing	reproduce	VERB
ijassa-497	25	14	faulty	faulty	ADJ
ijassa-497	25	15	conditions	condition	NOUN
ijassa-497	25	16	when	when	SCONJ
ijassa-497	25	17	we	we	PRON
ijassa-497	25	18	consider	consider	VERB
ijassa-497	25	19	a	a	DET
ijassa-497	25	20	machine	machine	NOUN
ijassa-497	25	21	diagnostic	diagnostic	ADJ
ijassa-497	25	22	task	task	NOUN
ijassa-497	25	23	.	.	PUNCT
ijassa-497	26	1	in	in	ADP
ijassa-497	26	2	the	the	DET
ijassa-497	26	3	light	light	NOUN
ijassa-497	26	4	of	of	ADP
ijassa-497	26	5	known	know	VERB
ijassa-497	26	6	and	and	CCONJ
ijassa-497	26	7	outlined	outline	VERB
ijassa-497	26	8	difficulties	difficulty	NOUN
ijassa-497	26	9	,	,	PUNCT
ijassa-497	26	10	classical	classical	ADJ
ijassa-497	26	11	methods	method	NOUN
ijassa-497	26	12	are	be	AUX
ijassa-497	26	13	not	not	PART
ijassa-497	26	14	applicable	applicable	ADJ
ijassa-497	26	15	to	to	ADP
ijassa-497	26	16	solving	solve	VERB
ijassa-497	26	17	these	these	DET
ijassa-497	26	18	problems	problem	NOUN
ijassa-497	26	19	,	,	PUNCT
ijassa-497	26	20	thus	thus	ADV
ijassa-497	26	21	a	a	DET
ijassa-497	26	22	variety	variety	NOUN
ijassa-497	26	23	of	of	ADP
ijassa-497	26	24	outlier	outlier	ADJ
ijassa-497	26	25	detection	detection	NOUN
ijassa-497	26	26	methods	method	NOUN
ijassa-497	26	27	have	have	AUX
ijassa-497	26	28	been	be	AUX
ijassa-497	26	29	developed	develop	VERB
ijassa-497	26	30	.	.	PUNCT
ijassa-497	27	1	such	such	ADJ
ijassa-497	27	2	problems	problem	NOUN
ijassa-497	27	3	justify	justify	VERB
ijassa-497	27	4	the	the	DET
ijassa-497	27	5	need	need	NOUN
ijassa-497	27	6	for	for	ADP
ijassa-497	27	7	specialized	specialized	ADJ
ijassa-497	27	8	approaches	approach	NOUN
ijassa-497	27	9	to	to	PART
ijassa-497	27	10	anomaly	anomaly	NOUN
ijassa-497	27	11	model	model	NOUN
ijassa-497	27	12	selection	selection	NOUN
ijassa-497	27	13	[	[	X
ijassa-497	27	14	2	2	NUM
ijassa-497	27	15	]	]	PUNCT
ijassa-497	27	16	,	,	PUNCT
ijassa-497	27	17	learning	learn	VERB
ijassa-497	27	18	with	with	ADP
ijassa-497	27	19	privileged	privileged	ADJ
ijassa-497	27	20	information	information	NOUN
ijassa-497	27	21	[	[	X
ijassa-497	27	22	6	6	NUM
ijassa-497	27	23	]	]	PUNCT
ijassa-497	27	24	,	,	PUNCT
ijassa-497	27	25	construction	construction	NOUN
ijassa-497	27	26	of	of	ADP
ijassa-497	27	27	ensembles	ensemble	NOUN
ijassa-497	27	28	of	of	ADP
ijassa-497	27	29	non	non	ADJ
ijassa-497	27	30	-	-	ADJ
ijassa-497	27	31	parametric	parametric	ADJ
ijassa-497	27	32	anomaly	anomaly	NOUN
ijassa-497	27	33	detectors	detector	NOUN
ijassa-497	27	34	in	in	ADP
ijassa-497	27	35	data	datum	NOUN
ijassa-497	27	36	streams	stream	NOUN
ijassa-497	27	37	[	[	X
ijassa-497	27	38	3,7,8	3,7,8	NUM
ijassa-497	27	39	]	]	PUNCT
ijassa-497	27	40	,	,	PUNCT
ijassa-497	27	41	usage	usage	NOUN
ijassa-497	27	42	of	of	ADP
ijassa-497	27	43	specific	specific	ADJ
ijassa-497	27	44	time	time	NOUN
ijassa-497	27	45	-	-	PUNCT
ijassa-497	27	46	series	series	NOUN
ijassa-497	27	47	models	model	NOUN
ijassa-497	27	48	[	[	X
ijassa-497	27	49	9–13	9–13	NOUN
ijassa-497	27	50	]	]	PUNCT
ijassa-497	27	51	,	,	PUNCT
ijassa-497	27	52	and	and	CCONJ
ijassa-497	27	53	explicit	explicit	ADJ
ijassa-497	27	54	rebalancing	rebalancing	NOUN
ijassa-497	27	55	of	of	ADP
ijassa-497	27	56	normal	normal	ADJ
ijassa-497	27	57	and	and	CCONJ
ijassa-497	27	58	abnormal	abnormal	ADJ
ijassa-497	27	59	classes	class	NOUN
ijassa-497	27	60	[	[	X
ijassa-497	27	61	4	4	NUM
ijassa-497	27	62	,	,	PUNCT
ijassa-497	27	63	5	5	NUM
ijassa-497	27	64	]	]	PUNCT
ijassa-497	27	65	,	,	PUNCT
ijassa-497	27	66	among	among	ADP
ijassa-497	27	67	others	other	NOUN
ijassa-497	27	68	.	.	PUNCT
ijassa-497	28	1	unsupervised	unsupervised	ADJ
ijassa-497	28	2	anomaly	anomaly	NOUN
ijassa-497	28	3	detection	detection	NOUN
ijassa-497	28	4	does	do	AUX
ijassa-497	28	5	not	not	PART
ijassa-497	28	6	require	require	VERB
ijassa-497	28	7	the	the	DET
ijassa-497	28	8	training	training	NOUN
ijassa-497	28	9	dataset	dataset	VERB
ijassa-497	28	10	to	to	PART
ijassa-497	28	11	be	be	AUX
ijassa-497	28	12	labelled	label	VERB
ijassa-497	28	13	,	,	PUNCT
ijassa-497	28	14	thus	thus	ADV
ijassa-497	28	15	it	it	PRON
ijassa-497	28	16	is	be	AUX
ijassa-497	28	17	applicable	applicable	ADJ
ijassa-497	28	18	to	to	ADP
ijassa-497	28	19	various	various	ADJ
ijassa-497	28	20	problems	problem	NOUN
ijassa-497	28	21	,	,	PUNCT
ijassa-497	28	22	as	as	SCONJ
ijassa-497	28	23	in	in	ADP
ijassa-497	28	24	general	general	ADJ
ijassa-497	28	25	it	it	PRON
ijassa-497	28	26	is	be	AUX
ijassa-497	28	27	not	not	PART
ijassa-497	28	28	feasible	feasible	ADJ
ijassa-497	28	29	to	to	PART
ijassa-497	28	30	collect	collect	VERB
ijassa-497	28	31	labels	label	NOUN
ijassa-497	28	32	.	.	PUNCT
ijassa-497	29	1	therefore	therefore	ADV
ijassa-497	29	2	a	a	DET
ijassa-497	29	3	number	number	NOUN
ijassa-497	29	4	of	of	ADP
ijassa-497	29	5	applications	application	NOUN
ijassa-497	29	6	adopts	adopt	VERB
ijassa-497	29	7	unsupervised	unsupervised	ADJ
ijassa-497	29	8	approaches	approach	NOUN
ijassa-497	29	9	,	,	PUNCT
ijassa-497	29	10	e.g.	e.g.	ADV
ijassa-497	29	11	based	base	VERB
ijassa-497	29	12	on	on	ADP
ijassa-497	29	13	density	density	NOUN
ijassa-497	29	14	estimation	estimation	NOUN
ijassa-497	29	15	or	or	CCONJ
ijassa-497	29	16	clustering	cluster	VERB
ijassa-497	29	17	[	[	X
ijassa-497	29	18	16	16	NUM
ijassa-497	29	19	]	]	PUNCT
ijassa-497	29	20	.	.	PUNCT
ijassa-497	30	1	according	accord	VERB
ijassa-497	30	2	to	to	ADP
ijassa-497	30	3	the	the	DET
ijassa-497	30	4	surveys	survey	NOUN
ijassa-497	30	5	[	[	X
ijassa-497	30	6	15	15	NUM
ijassa-497	30	7	]	]	PUNCT
ijassa-497	30	8	and	and	CCONJ
ijassa-497	30	9	[	[	X
ijassa-497	30	10	17	17	NUM
ijassa-497	30	11	]	]	X
ijassa-497	30	12	unsupervised	unsupervised	ADJ
ijassa-497	30	13	anomaly	anomaly	NOUN
ijassa-497	30	14	detection	detection	NOUN
ijassa-497	30	15	techniques	technique	NOUN
ijassa-497	30	16	could	could	AUX
ijassa-497	30	17	be	be	AUX
ijassa-497	30	18	generally	generally	ADV
ijassa-497	30	19	categorized	categorize	VERB
ijassa-497	30	20	as	as	ADP
ijassa-497	30	21	probabilistic	probabilistic	ADJ
ijassa-497	30	22	(	(	PUNCT
ijassa-497	30	23	distributionand	distributionand	NOUN
ijassa-497	30	24	density	density	NOUN
ijassa-497	30	25	-	-	PUNCT
ijassa-497	30	26	based	base	VERB
ijassa-497	30	27	)	)	PUNCT
ijassa-497	30	28	,	,	PUNCT
ijassa-497	30	29	prediction	prediction	NOUN
ijassa-497	30	30	-	-	PUNCT
ijassa-497	30	31	based	base	VERB
ijassa-497	30	32	,	,	PUNCT
ijassa-497	30	33	distance	distance	NOUN
ijassa-497	30	34	-	-	PUNCT
ijassa-497	30	35	based	base	VERB
ijassa-497	30	36	,	,	PUNCT
ijassa-497	30	37	classification	classification	NOUN
ijassa-497	30	38	-	-	PUNCT
ijassa-497	30	39	based	base	VERB
ijassa-497	30	40	,	,	PUNCT
ijassa-497	30	41	clustering	clustering	NOUN
ijassa-497	30	42	-	-	PUNCT
ijassa-497	30	43	based	base	VERB
ijassa-497	30	44	and	and	CCONJ
ijassa-497	30	45	information	information	NOUN
ijassa-497	30	46	-	-	PUNCT
ijassa-497	30	47	theoretic	theoretic	NOUN
ijassa-497	30	48	approaches	approach	NOUN
ijassa-497	30	49	.	.	PUNCT
ijassa-497	31	1	distribution	distribution	NOUN
ijassa-497	31	2	-	-	PUNCT
ijassa-497	31	3	based	base	VERB
ijassa-497	31	4	methods	method	NOUN
ijassa-497	31	5	estimate	estimate	VERB
ijassa-497	31	6	parameters	parameter	NOUN
ijassa-497	31	7	of	of	ADP
ijassa-497	31	8	a	a	DET
ijassa-497	31	9	target	target	NOUN
ijassa-497	31	10	data	datum	NOUN
ijassa-497	31	11	distribution	distribution	NOUN
ijassa-497	31	12	and	and	CCONJ
ijassa-497	31	13	determine	determine	VERB
ijassa-497	31	14	whether	whether	SCONJ
ijassa-497	31	15	a	a	DET
ijassa-497	31	16	test	test	NOUN
ijassa-497	31	17	object	object	NOUN
ijassa-497	31	18	comes	come	VERB
ijassa-497	31	19	from	from	ADP
ijassa-497	31	20	the	the	DET
ijassa-497	31	21	same	same	ADJ
ijassa-497	31	22	distribution	distribution	NOUN
ijassa-497	31	23	that	that	PRON
ijassa-497	31	24	generated	generate	VERB
ijassa-497	31	25	samples	sample	NOUN
ijassa-497	31	26	from	from	ADP
ijassa-497	31	27	the	the	DET
ijassa-497	31	28	training	training	NOUN
ijassa-497	31	29	set	set	NOUN
ijassa-497	31	30	.	.	PUNCT
ijassa-497	32	1	the	the	DET
ijassa-497	32	2	main	main	ADJ
ijassa-497	32	3	drawback	drawback	NOUN
ijassa-497	32	4	of	of	ADP
ijassa-497	32	5	these	these	DET
ijassa-497	32	6	methods	method	NOUN
ijassa-497	32	7	is	be	AUX
ijassa-497	32	8	the	the	DET
ijassa-497	32	9	necessity	necessity	NOUN
ijassa-497	32	10	to	to	PART
ijassa-497	32	11	select	select	VERB
ijassa-497	32	12	some	some	DET
ijassa-497	32	13	parametric	parametric	ADJ
ijassa-497	32	14	class	class	NOUN
ijassa-497	32	15	of	of	ADP
ijassa-497	32	16	data	datum	NOUN
ijassa-497	32	17	distributions	distribution	NOUN
ijassa-497	32	18	.	.	PUNCT
ijassa-497	33	1	one	one	NUM
ijassa-497	33	2	of	of	ADP
ijassa-497	33	3	the	the	DET
ijassa-497	33	4	tricks	trick	NOUN
ijassa-497	33	5	is	be	AUX
ijassa-497	33	6	to	to	PART
ijassa-497	33	7	model	model	VERB
ijassa-497	33	8	the	the	DET
ijassa-497	33	9	target	target	NOUN
ijassa-497	33	10	distribution	distribution	NOUN
ijassa-497	33	11	as	as	ADP
ijassa-497	33	12	a	a	DET
ijassa-497	33	13	mixture	mixture	NOUN
ijassa-497	33	14	of	of	ADP
ijassa-497	33	15	gaussians	gaussian	NOUN
ijassa-497	33	16	,	,	PUNCT
ijassa-497	33	17	however	however	ADV
ijassa-497	33	18	the	the	DET
ijassa-497	33	19	number	number	NOUN
ijassa-497	33	20	of	of	ADP
ijassa-497	33	21	gaussians	gaussian	NOUN
ijassa-497	33	22	still	still	ADV
ijassa-497	33	23	have	have	VERB
ijassa-497	33	24	to	to	PART
ijassa-497	33	25	be	be	AUX
ijassa-497	33	26	determined	determine	VERB
ijassa-497	33	27	.	.	PUNCT
ijassa-497	34	1	to	to	PART
ijassa-497	34	2	mitigate	mitigate	VERB
ijassa-497	34	3	this	this	DET
ijassa-497	34	4	problem	problem	NOUN
ijassa-497	34	5	,	,	PUNCT
ijassa-497	34	6	other	other	ADJ
ijassa-497	34	7	non	non	ADJ
ijassa-497	34	8	-	-	ADJ
ijassa-497	34	9	parametric	parametric	ADJ
ijassa-497	34	10	techniques	technique	NOUN
ijassa-497	34	11	could	could	AUX
ijassa-497	34	12	be	be	AUX
ijassa-497	34	13	utilized	utilize	VERB
ijassa-497	34	14	,	,	PUNCT
ijassa-497	34	15	for	for	ADP
ijassa-497	34	16	instance	instance	NOUN
ijassa-497	34	17	histogram	histogram	NOUN
ijassa-497	34	18	-	-	PUNCT
ijassa-497	34	19	based	base	VERB
ijassa-497	34	20	or	or	CCONJ
ijassa-497	34	21	kernel	kernel	PROPN
ijassa-497	34	22	density	density	PROPN
ijassa-497	34	23	estimator	estimator	NOUN
ijassa-497	34	24	(	(	PUNCT
ijassa-497	34	25	kde	kde	PROPN
ijassa-497	34	26	)	)	PUNCT
ijassa-497	34	27	.	.	PUNCT
ijassa-497	35	1	however	however	ADV
ijassa-497	35	2	,	,	PUNCT
ijassa-497	35	3	the	the	DET
ijassa-497	35	4	number	number	NOUN
ijassa-497	35	5	of	of	ADP
ijassa-497	35	6	bins	bin	NOUN
ijassa-497	35	7	should	should	AUX
ijassa-497	35	8	be	be	AUX
ijassa-497	35	9	firstly	firstly	ADV
ijassa-497	35	10	specified	specify	VERB
ijassa-497	35	11	,	,	PUNCT
ijassa-497	35	12	and	and	CCONJ
ijassa-497	35	13	the	the	DET
ijassa-497	35	14	performance	performance	NOUN
ijassa-497	35	15	is	be	AUX
ijassa-497	35	16	highly	highly	ADV
ijassa-497	35	17	sensitive	sensitive	ADJ
ijassa-497	35	18	to	to	ADP
ijassa-497	35	19	this	this	DET
ijassa-497	35	20	hyper	hyper	NOUN
ijassa-497	35	21	-	-	NOUN
ijassa-497	35	22	parameter	parameter	NOUN
ijassa-497	35	23	.	.	PUNCT
ijassa-497	36	1	for	for	ADP
ijassa-497	36	2	multivariate	multivariate	NOUN
ijassa-497	36	3	problems	problem	NOUN
ijassa-497	36	4	a	a	DET
ijassa-497	36	5	basic	basic	ADJ
ijassa-497	36	6	approach	approach	NOUN
ijassa-497	36	7	is	be	AUX
ijassa-497	36	8	to	to	PART
ijassa-497	36	9	estimate	estimate	VERB
ijassa-497	36	10	a	a	DET
ijassa-497	36	11	histogram	histogram	NOUN
ijassa-497	36	12	per	per	ADP
ijassa-497	36	13	each	each	DET
ijassa-497	36	14	input	input	NOUN
ijassa-497	36	15	feature	feature	NOUN
ijassa-497	36	16	.	.	PUNCT
ijassa-497	37	1	however	however	ADV
ijassa-497	37	2	some	some	DET
ijassa-497	37	3	features	feature	NOUN
ijassa-497	37	4	could	could	AUX
ijassa-497	37	5	be	be	AUX
ijassa-497	37	6	correlated	correlate	VERB
ijassa-497	37	7	,	,	PUNCT
ijassa-497	37	8	in	in	ADP
ijassa-497	37	9	that	that	DET
ijassa-497	37	10	case	case	NOUN
ijassa-497	37	11	the	the	DET
ijassa-497	37	12	information	information	NOUN
ijassa-497	37	13	about	about	ADP
ijassa-497	37	14	such	such	ADJ
ijassa-497	37	15	dependency	dependency	NOUN
ijassa-497	37	16	will	will	AUX
ijassa-497	37	17	be	be	AUX
ijassa-497	37	18	lost	lose	VERB
ijassa-497	37	19	.	.	PUNCT
ijassa-497	38	1	prediction	prediction	NOUN
ijassa-497	38	2	-	-	PUNCT
ijassa-497	38	3	based	base	VERB
ijassa-497	38	4	techniques	technique	NOUN
ijassa-497	38	5	predict	predict	VERB
ijassa-497	38	6	future	future	ADJ
ijassa-497	38	7	observations	observation	NOUN
ijassa-497	38	8	based	base	VERB
ijassa-497	38	9	on	on	ADP
ijassa-497	38	10	previous	previous	ADJ
ijassa-497	38	11	items	item	NOUN
ijassa-497	38	12	and	and	CCONJ
ijassa-497	38	13	then	then	ADV
ijassa-497	38	14	compare	compare	AUX
ijassa-497	38	15	predicted	predict	VERB
ijassa-497	38	16	and	and	CCONJ
ijassa-497	38	17	real	real	ADJ
ijassa-497	38	18	data	datum	NOUN
ijassa-497	38	19	to	to	PART
ijassa-497	38	20	identify	identify	VERB
ijassa-497	38	21	anomalies	anomaly	NOUN
ijassa-497	38	22	.	.	PUNCT
ijassa-497	39	1	another	another	DET
ijassa-497	39	2	type	type	NOUN
ijassa-497	39	3	of	of	ADP
ijassa-497	39	4	approaches	approach	NOUN
ijassa-497	39	5	is	be	AUX
ijassa-497	39	6	based	base	VERB
ijassa-497	39	7	on	on	ADP
ijassa-497	39	8	the	the	DET
ijassa-497	39	9	distance	distance	NOUN
ijassa-497	39	10	to	to	ADP
ijassa-497	39	11	the	the	DET
ijassa-497	39	12	k	k	NOUN
ijassa-497	39	13	-	-	PUNCT
ijassa-497	39	14	th	th	X
ijassa-497	39	15	nearest	near	ADJ
ijassa-497	39	16	neighbour	neighbour	NOUN
ijassa-497	39	17	(	(	PUNCT
ijassa-497	39	18	knn	knn	PROPN
ijassa-497	39	19	)	)	PUNCT
ijassa-497	39	20	.	.	PUNCT
ijassa-497	40	1	one	one	NUM
ijassa-497	40	2	of	of	ADP
ijassa-497	40	3	such	such	ADJ
ijassa-497	40	4	techniques	technique	NOUN
ijassa-497	40	5	is	be	AUX
ijassa-497	40	6	the	the	DET
ijassa-497	40	7	knn	knn	PROPN
ijassa-497	40	8	-	-	PUNCT
ijassa-497	40	9	based	base	VERB
ijassa-497	40	10	outlier	outlier	NOUN
ijassa-497	40	11	detector	detector	NOUN
ijassa-497	40	12	[	[	X
ijassa-497	40	13	18	18	NUM
ijassa-497	40	14	]	]	PUNCT
ijassa-497	40	15	.	.	PUNCT
ijassa-497	41	1	all	all	DET
ijassa-497	41	2	objects	object	NOUN
ijassa-497	41	3	are	be	AUX
ijassa-497	41	4	sorted	sort	VERB
ijassa-497	41	5	w.r.t	w.r.t	NOUN
ijassa-497	41	6	.	.	PUNCT
ijassa-497	42	1	the	the	DET
ijassa-497	42	2	average	average	ADJ
ijassa-497	42	3	distance	distance	NOUN
ijassa-497	42	4	to	to	ADP
ijassa-497	42	5	k	k	PROPN
ijassa-497	42	6	nearest	near	ADJ
ijassa-497	42	7	neighbours	neighbour	NOUN
ijassa-497	42	8	and	and	CCONJ
ijassa-497	42	9	top	top	ADJ
ijassa-497	42	10	n	n	NOUN
ijassa-497	42	11	of	of	ADP
ijassa-497	42	12	them	they	PRON
ijassa-497	42	13	with	with	ADP
ijassa-497	42	14	the	the	DET
ijassa-497	42	15	highest	high	ADJ
ijassa-497	42	16	average	average	ADJ
ijassa-497	42	17	distance	distance	NOUN
ijassa-497	42	18	are	be	AUX
ijassa-497	42	19	claimed	claim	VERB
ijassa-497	42	20	to	to	PART
ijassa-497	42	21	be	be	AUX
ijassa-497	42	22	anomalies	anomaly	NOUN
ijassa-497	42	23	.	.	PUNCT
ijassa-497	43	1	lof	lof	PROPN
ijassa-497	43	2	method	method	PROPN
ijassa-497	43	3	,	,	PUNCT
ijassa-497	43	4	proposed	propose	VERB
ijassa-497	43	5	in	in	ADP
ijassa-497	43	6	[	[	X
ijassa-497	43	7	19	19	NUM
ijassa-497	43	8	]	]	PUNCT
ijassa-497	43	9	,	,	PUNCT
ijassa-497	43	10	exploits	exploit	VERB
ijassa-497	43	11	density	density	NOUN
ijassa-497	43	12	based	base	VERB
ijassa-497	43	13	approach	approach	NOUN
ijassa-497	43	14	:	:	PUNCT
ijassa-497	43	15	it	it	PRON
ijassa-497	43	16	uses	use	VERB
ijassa-497	43	17	the	the	DET
ijassa-497	43	18	distance	distance	NOUN
ijassa-497	43	19	to	to	ADP
ijassa-497	43	20	the	the	DET
ijassa-497	43	21	k	k	NOUN
ijassa-497	43	22	-	-	PUNCT
ijassa-497	43	23	th	th	X
ijassa-497	43	24	nearest	near	ADJ
ijassa-497	43	25	neighbour	neighbour	NOUN
ijassa-497	43	26	as	as	ADP
ijassa-497	43	27	an	an	DET
ijassa-497	43	28	inverse	inverse	NOUN
ijassa-497	43	29	estimate	estimate	NOUN
ijassa-497	43	30	of	of	ADP
ijassa-497	43	31	a	a	DET
ijassa-497	43	32	local	local	ADJ
ijassa-497	43	33	density	density	NOUN
ijassa-497	43	34	value	value	NOUN
ijassa-497	43	35	.	.	PUNCT
ijassa-497	44	1	however	however	SCONJ
ijassa-497	44	2	data	datum	NOUN
ijassa-497	44	3	could	could	AUX
ijassa-497	44	4	contain	contain	VERB
ijassa-497	44	5	clusters	cluster	NOUN
ijassa-497	44	6	with	with	ADP
ijassa-497	44	7	different	different	ADJ
ijassa-497	44	8	densities	density	NOUN
ijassa-497	44	9	leading	lead	VERB
ijassa-497	44	10	to	to	ADP
ijassa-497	44	11	significantly	significantly	ADV
ijassa-497	44	12	increased	increase	VERB
ijassa-497	44	13	false	false	ADJ
ijassa-497	44	14	anomaly	anomaly	NOUN
ijassa-497	44	15	detections	detection	NOUN
ijassa-497	44	16	.	.	PUNCT
ijassa-497	45	1	main	main	ADJ
ijassa-497	45	2	drawback	drawback	NOUN
ijassa-497	45	3	of	of	ADP
ijassa-497	45	4	distances	distance	NOUN
ijassa-497	45	5	-	-	PUNCT
ijassa-497	45	6	based	base	VERB
ijassa-497	45	7	anomaly	anomaly	NOUN
ijassa-497	45	8	detection	detection	NOUN
ijassa-497	45	9	methods	method	NOUN
ijassa-497	45	10	is	be	AUX
ijassa-497	45	11	poor	poor	ADJ
ijassa-497	45	12	interpretability	interpretability	NOUN
ijassa-497	45	13	of	of	ADP
ijassa-497	45	14	their	their	PRON
ijassa-497	45	15	output	output	NOUN
ijassa-497	45	16	.	.	PUNCT
ijassa-497	46	1	to	to	PART
ijassa-497	46	2	address	address	VERB
ijassa-497	46	3	this	this	DET
ijassa-497	46	4	issue	issue	NOUN
ijassa-497	46	5	conformal	conformal	NOUN
ijassa-497	46	6	anomaly	anomaly	NOUN
ijassa-497	46	7	detector	detector	NOUN
ijassa-497	46	8	(	(	PUNCT
ijassa-497	46	9	cad	cad	NOUN
ijassa-497	46	10	)	)	PUNCT
ijassa-497	46	11	was	be	AUX
ijassa-497	46	12	proposed	propose	VERB
ijassa-497	46	13	by	by	ADP
ijassa-497	46	14	laxhammar	laxhammar	NOUN
ijassa-497	46	15	[	[	X
ijassa-497	46	16	16	16	NUM
ijassa-497	46	17	]	]	PUNCT
ijassa-497	46	18	.	.	PUNCT
ijassa-497	47	1	having	have	VERB
ijassa-497	47	2	a	a	DET
ijassa-497	47	3	probabilistic	probabilistic	ADJ
ijassa-497	47	4	interpretation	interpretation	NOUN
ijassa-497	47	5	of	of	ADP
ijassa-497	47	6	the	the	DET
ijassa-497	47	7	degree	degree	NOUN
ijassa-497	47	8	of	of	ADP
ijassa-497	47	9	anomalousness	anomalousness	NOUN
ijassa-497	47	10	allows	allow	VERB
ijassa-497	47	11	choosing	choose	VERB
ijassa-497	47	12	a	a	DET
ijassa-497	47	13	threshold	threshold	NOUN
ijassa-497	47	14	with	with	ADP
ijassa-497	47	15	a	a	DET
ijassa-497	47	16	false	false	ADJ
ijassa-497	47	17	alarm	alarm	NOUN
ijassa-497	47	18	rate	rate	NOUN
ijassa-497	47	19	guarantee	guarantee	NOUN
ijassa-497	47	20	.	.	PUNCT
ijassa-497	48	1	zhao	zhao	NOUN
ijassa-497	48	2	and	and	CCONJ
ijassa-497	48	3	saligrama	saligrama	NOUN
ijassa-497	48	4	stated	state	VERB
ijassa-497	48	5	in	in	ADP
ijassa-497	48	6	[	[	X
ijassa-497	48	7	20	20	NUM
ijassa-497	48	8	]	]	PUNCT
ijassa-497	48	9	that	that	SCONJ
ijassa-497	48	10	“	"	PUNCT
ijassa-497	48	11	while	while	SCONJ
ijassa-497	48	12	[	[	X
ijassa-497	48	13	modern	modern	ADJ
ijassa-497	48	14	anomaly	anomaly	NOUN
ijassa-497	48	15	detection	detection	NOUN
ijassa-497	48	16	]	]	PUNCT
ijassa-497	48	17	approaches	approach	NOUN
ijassa-497	48	18	provide	provide	VERB
ijassa-497	48	19	impressive	impressive	ADJ
ijassa-497	48	20	computationally	computationally	ADV
ijassa-497	48	21	efficient	efficient	ADJ
ijassa-497	48	22	solutions	solution	NOUN
ijassa-497	48	23	on	on	ADP
ijassa-497	48	24	real	real	ADJ
ijassa-497	48	25	data	datum	NOUN
ijassa-497	48	26	,	,	PUNCT
ijassa-497	48	27	it	it	PRON
ijassa-497	48	28	is	be	AUX
ijassa-497	48	29	generally	generally	ADV
ijassa-497	48	30	difficult	difficult	ADJ
ijassa-497	48	31	to	to	PART
ijassa-497	48	32	precisely	precisely	ADV
ijassa-497	48	33	relate	relate	VERB
ijassa-497	48	34	tuning	tune	VERB
ijassa-497	48	35	parameter	parameter	NOUN
ijassa-497	48	36	choices	choice	NOUN
ijassa-497	48	37	to	to	PART
ijassa-497	48	38	desired	desire	VERB
ijassa-497	48	39	false	false	ADJ
ijassa-497	48	40	alarm	alarm	NOUN
ijassa-497	48	41	probability	probability	NOUN
ijassa-497	48	42	”	"	PUNCT
ijassa-497	48	43	.	.	PUNCT
ijassa-497	49	1	at	at	ADP
ijassa-497	49	2	the	the	DET
ijassa-497	49	3	same	same	ADJ
ijassa-497	49	4	time	time	NOUN
ijassa-497	49	5	according	accord	VERB
ijassa-497	49	6	to	to	ADP
ijassa-497	49	7	burnaev	burnaev	PROPN
ijassa-497	49	8	and	and	CCONJ
ijassa-497	49	9	nazarov	nazarov	PROPN
ijassa-497	49	10	,	,	PUNCT
ijassa-497	49	11	conformal	conformal	ADJ
ijassa-497	49	12	prediction	prediction	NOUN
ijassa-497	49	13	could	could	AUX
ijassa-497	49	14	be	be	AUX
ijassa-497	49	15	used	use	VERB
ijassa-497	49	16	for	for	ADP
ijassa-497	49	17	constructing	construct	VERB
ijassa-497	49	18	non	non	ADJ
ijassa-497	49	19	-	-	ADJ
ijassa-497	49	20	parametric	parametric	ADJ
ijassa-497	49	21	confidence	confidence	NOUN
ijassa-497	49	22	intervals	interval	NOUN
ijassa-497	49	23	with	with	ADP
ijassa-497	49	24	a	a	DET
ijassa-497	49	25	specified	specify	VERB
ijassa-497	49	26	confidence	confidence	NOUN
ijassa-497	49	27	probability	probability	NOUN
ijassa-497	49	28	[	[	X
ijassa-497	49	29	21	21	NUM
ijassa-497	49	30	,	,	PUNCT
ijassa-497	49	31	22	22	NUM
ijassa-497	49	32	]	]	PUNCT
ijassa-497	49	33	.	.	PUNCT
ijassa-497	50	1	copyright	copyright	NOUN
ijassa-497	50	2	©	©	PROPN
ijassa-497	50	3	2017	2017	NUM
ijassa-497	50	4	assa	assa	NOUN
ijassa-497	50	5	.	.	PUNCT
ijassa-497	51	1	adv	adv	PROPN
ijassa-497	51	2	syst	syst	PROPN
ijassa-497	51	3	sci	sci	PROPN
ijassa-497	51	4	appl	appl	PROPN
ijassa-497	51	5	(	(	PUNCT
ijassa-497	51	6	2017	2017	NUM
ijassa-497	51	7	)	)	PUNCT
ijassa-497	51	8	24	24	NUM
ijassa-497	51	9	aleksandr	aleksandr	PROPN
ijassa-497	51	10	safin	safin	PROPN
ijassa-497	51	11	,	,	PUNCT
ijassa-497	51	12	evgeny	evgeny	PROPN
ijassa-497	51	13	burnaev	burnaev	PROPN
ijassa-497	52	1	some	some	DET
ijassa-497	52	2	techniques	technique	NOUN
ijassa-497	52	3	adopt	adopt	VERB
ijassa-497	52	4	approaches	approach	NOUN
ijassa-497	52	5	used	use	VERB
ijassa-497	52	6	for	for	ADP
ijassa-497	52	7	classification	classification	NOUN
ijassa-497	52	8	tasks	task	NOUN
ijassa-497	52	9	.	.	PUNCT
ijassa-497	53	1	tax	tax	NOUN
ijassa-497	53	2	and	and	CCONJ
ijassa-497	53	3	duin	duin	PROPN
ijassa-497	53	4	proposed	propose	VERB
ijassa-497	53	5	support	support	NOUN
ijassa-497	53	6	vector	vector	NOUN
ijassa-497	53	7	data	datum	NOUN
ijassa-497	53	8	description	description	NOUN
ijassa-497	53	9	[	[	X
ijassa-497	53	10	23	23	NUM
ijassa-497	53	11	]	]	PUNCT
ijassa-497	53	12	and	and	CCONJ
ijassa-497	53	13	later	later	ADV
ijassa-497	53	14	it	it	PRON
ijassa-497	53	15	was	be	AUX
ijassa-497	53	16	refined	refine	VERB
ijassa-497	53	17	in	in	ADP
ijassa-497	53	18	[	[	X
ijassa-497	53	19	24	24	NUM
ijassa-497	53	20	]	]	PUNCT
ijassa-497	53	21	.	.	PUNCT
ijassa-497	54	1	the	the	DET
ijassa-497	54	2	task	task	NOUN
ijassa-497	54	3	of	of	ADP
ijassa-497	54	4	data	datum	NOUN
ijassa-497	54	5	description	description	NOUN
ijassa-497	54	6	is	be	AUX
ijassa-497	54	7	formulated	formulate	VERB
ijassa-497	54	8	as	as	SCONJ
ijassa-497	54	9	follows	follow	VERB
ijassa-497	54	10	:	:	PUNCT
ijassa-497	54	11	given	give	VERB
ijassa-497	54	12	the	the	DET
ijassa-497	54	13	unlabelled	unlabelled	ADJ
ijassa-497	54	14	training	training	NOUN
ijassa-497	54	15	data	datum	NOUN
ijassa-497	54	16	,	,	PUNCT
ijassa-497	54	17	construct	construct	VERB
ijassa-497	54	18	a	a	DET
ijassa-497	54	19	closed	closed	ADJ
ijassa-497	54	20	boundary	boundary	NOUN
ijassa-497	54	21	(	(	PUNCT
ijassa-497	54	22	or	or	CCONJ
ijassa-497	54	23	a	a	DET
ijassa-497	54	24	set	set	NOUN
ijassa-497	54	25	of	of	ADP
ijassa-497	54	26	them	they	PRON
ijassa-497	54	27	)	)	PUNCT
ijassa-497	54	28	that	that	PRON
ijassa-497	54	29	contains	contain	VERB
ijassa-497	54	30	predominantly	predominantly	ADV
ijassa-497	54	31	the	the	DET
ijassa-497	54	32	target	target	NOUN
ijassa-497	54	33	data	datum	NOUN
ijassa-497	54	34	,	,	PUNCT
ijassa-497	54	35	and	and	CCONJ
ijassa-497	54	36	outliers	outlier	NOUN
ijassa-497	54	37	are	be	AUX
ijassa-497	54	38	outside	outside	ADP
ijassa-497	54	39	this	this	DET
ijassa-497	54	40	boundary	boundary	NOUN
ijassa-497	54	41	.	.	PUNCT
ijassa-497	55	1	in	in	ADP
ijassa-497	55	2	a	a	DET
ijassa-497	55	3	simple	simple	ADJ
ijassa-497	55	4	case	case	NOUN
ijassa-497	55	5	,	,	PUNCT
ijassa-497	55	6	boundary	boundary	NOUN
ijassa-497	55	7	is	be	AUX
ijassa-497	55	8	supposed	suppose	VERB
ijassa-497	55	9	to	to	PART
ijassa-497	55	10	be	be	AUX
ijassa-497	55	11	spherical	spherical	ADJ
ijassa-497	55	12	,	,	PUNCT
ijassa-497	55	13	but	but	CCONJ
ijassa-497	55	14	in	in	ADP
ijassa-497	55	15	general	general	ADJ
ijassa-497	55	16	it	it	PRON
ijassa-497	55	17	is	be	AUX
ijassa-497	55	18	possible	possible	ADJ
ijassa-497	55	19	to	to	PART
ijassa-497	55	20	determine	determine	VERB
ijassa-497	55	21	an	an	DET
ijassa-497	55	22	arbitrary	arbitrary	ADV
ijassa-497	55	23	-	-	PUNCT
ijassa-497	55	24	shaped	shape	VERB
ijassa-497	55	25	flexible	flexible	ADJ
ijassa-497	55	26	boundary	boundary	NOUN
ijassa-497	55	27	by	by	ADP
ijassa-497	55	28	using	use	VERB
ijassa-497	55	29	kernel	kernel	NOUN
ijassa-497	55	30	functions	function	NOUN
ijassa-497	55	31	.	.	PUNCT
ijassa-497	56	1	moreover	moreover	ADV
ijassa-497	56	2	,	,	PUNCT
ijassa-497	56	3	svdd	svdd	PROPN
ijassa-497	56	4	is	be	AUX
ijassa-497	56	5	robust	robust	ADJ
ijassa-497	56	6	against	against	ADP
ijassa-497	56	7	the	the	DET
ijassa-497	56	8	training	training	NOUN
ijassa-497	56	9	data	datum	NOUN
ijassa-497	56	10	containing	contain	VERB
ijassa-497	56	11	outliers	outlier	NOUN
ijassa-497	56	12	and	and	CCONJ
ijassa-497	56	13	also	also	ADV
ijassa-497	56	14	is	be	AUX
ijassa-497	56	15	capable	capable	ADJ
ijassa-497	56	16	of	of	ADP
ijassa-497	56	17	improving	improve	VERB
ijassa-497	56	18	the	the	DET
ijassa-497	56	19	accuracy	accuracy	NOUN
ijassa-497	56	20	by	by	ADP
ijassa-497	56	21	incorporating	incorporate	VERB
ijassa-497	56	22	additional	additional	ADJ
ijassa-497	56	23	information	information	NOUN
ijassa-497	56	24	about	about	ADP
ijassa-497	56	25	negative	negative	ADJ
ijassa-497	56	26	examples	example	NOUN
ijassa-497	56	27	,	,	PUNCT
ijassa-497	56	28	in	in	ADP
ijassa-497	56	29	case	case	NOUN
ijassa-497	56	30	when	when	SCONJ
ijassa-497	56	31	the	the	DET
ijassa-497	56	32	training	training	NOUN
ijassa-497	56	33	dataset	dataset	NOUN
ijassa-497	56	34	is	be	AUX
ijassa-497	56	35	labelled	label	VERB
ijassa-497	56	36	.	.	PUNCT
ijassa-497	57	1	according	accord	VERB
ijassa-497	57	2	to	to	ADP
ijassa-497	57	3	the	the	DET
ijassa-497	57	4	results	result	NOUN
ijassa-497	57	5	of	of	ADP
ijassa-497	57	6	their	their	PRON
ijassa-497	57	7	study	study	NOUN
ijassa-497	57	8	,	,	PUNCT
ijassa-497	57	9	svdd	svdd	PROPN
ijassa-497	57	10	is	be	AUX
ijassa-497	57	11	shown	show	VERB
ijassa-497	57	12	to	to	PART
ijassa-497	57	13	yield	yield	VERB
ijassa-497	57	14	mostly	mostly	ADV
ijassa-497	57	15	comparable	comparable	ADJ
ijassa-497	57	16	or	or	CCONJ
ijassa-497	57	17	even	even	ADV
ijassa-497	57	18	better	well	ADJ
ijassa-497	57	19	results	result	NOUN
ijassa-497	57	20	for	for	ADP
ijassa-497	57	21	sparse	sparse	ADJ
ijassa-497	57	22	and	and	CCONJ
ijassa-497	57	23	complex	complex	ADJ
ijassa-497	57	24	multidimensional	multidimensional	ADJ
ijassa-497	57	25	datasets	dataset	NOUN
ijassa-497	57	26	.	.	PUNCT
ijassa-497	58	1	an	an	DET
ijassa-497	58	2	extension	extension	NOUN
ijassa-497	58	3	of	of	ADP
ijassa-497	58	4	svm	svm	NOUN
ijassa-497	58	5	to	to	ADP
ijassa-497	58	6	the	the	DET
ijassa-497	58	7	case	case	NOUN
ijassa-497	58	8	of	of	ADP
ijassa-497	58	9	unlabelled	unlabelled	ADJ
ijassa-497	58	10	data	datum	NOUN
ijassa-497	58	11	is	be	AUX
ijassa-497	58	12	outlined	outline	VERB
ijassa-497	58	13	in	in	ADP
ijassa-497	58	14	[	[	X
ijassa-497	58	15	25	25	NUM
ijassa-497	58	16	]	]	PUNCT
ijassa-497	58	17	.	.	PUNCT
ijassa-497	59	1	this	this	DET
ijassa-497	59	2	approach	approach	NOUN
ijassa-497	59	3	which	which	PRON
ijassa-497	59	4	is	be	AUX
ijassa-497	59	5	referred	refer	VERB
ijassa-497	59	6	to	to	ADP
ijassa-497	59	7	as	as	SCONJ
ijassa-497	59	8	one	one	NUM
ijassa-497	59	9	-	-	PUNCT
ijassa-497	59	10	class	class	NOUN
ijassa-497	59	11	svm	svm	NOUN
ijassa-497	59	12	has	have	AUX
ijassa-497	59	13	been	be	AUX
ijassa-497	59	14	adapted	adapt	VERB
ijassa-497	59	15	by	by	ADP
ijassa-497	59	16	ma	ma	PROPN
ijassa-497	59	17	and	and	CCONJ
ijassa-497	59	18	perkins	perkin	VERB
ijassa-497	60	1	[	[	X
ijassa-497	60	2	26	26	NUM
ijassa-497	60	3	]	]	PUNCT
ijassa-497	60	4	for	for	ADP
ijassa-497	60	5	time	time	NOUN
ijassa-497	60	6	series	series	NOUN
ijassa-497	60	7	.	.	PUNCT
ijassa-497	61	1	the	the	DET
ijassa-497	61	2	problem	problem	NOUN
ijassa-497	61	3	of	of	ADP
ijassa-497	61	4	anomaly	anomaly	NOUN
ijassa-497	61	5	detection	detection	NOUN
ijassa-497	61	6	has	have	VERB
ijassa-497	61	7	many	many	ADJ
ijassa-497	61	8	dimensions	dimension	NOUN
ijassa-497	61	9	.	.	PUNCT
ijassa-497	62	1	in	in	ADP
ijassa-497	62	2	particular	particular	ADJ
ijassa-497	62	3	,	,	PUNCT
ijassa-497	62	4	data	datum	NOUN
ijassa-497	62	5	could	could	AUX
ijassa-497	62	6	be	be	AUX
ijassa-497	62	7	not	not	PART
ijassa-497	62	8	fixed	fix	VERB
ijassa-497	62	9	but	but	CCONJ
ijassa-497	62	10	represented	represent	VERB
ijassa-497	62	11	as	as	ADP
ijassa-497	62	12	a	a	DET
ijassa-497	62	13	stream	stream	NOUN
ijassa-497	62	14	.	.	PUNCT
ijassa-497	63	1	a	a	DET
ijassa-497	63	2	variety	variety	NOUN
ijassa-497	63	3	of	of	ADP
ijassa-497	63	4	techniques	technique	NOUN
ijassa-497	63	5	could	could	AUX
ijassa-497	63	6	be	be	AUX
ijassa-497	63	7	used	use	VERB
ijassa-497	63	8	for	for	ADP
ijassa-497	63	9	anomaly	anomaly	NOUN
ijassa-497	63	10	detection	detection	NOUN
ijassa-497	63	11	.	.	PUNCT
ijassa-497	64	1	however	however	ADV
ijassa-497	64	2	,	,	PUNCT
ijassa-497	64	3	only	only	ADV
ijassa-497	64	4	a	a	DET
ijassa-497	64	5	few	few	ADJ
ijassa-497	64	6	can	can	AUX
ijassa-497	64	7	be	be	AUX
ijassa-497	64	8	used	use	VERB
ijassa-497	64	9	for	for	ADP
ijassa-497	64	10	data	datum	NOUN
ijassa-497	64	11	streams	stream	NOUN
ijassa-497	64	12	.	.	PUNCT
ijassa-497	65	1	classical	classical	ADJ
ijassa-497	65	2	algorithms	algorithm	NOUN
ijassa-497	65	3	for	for	ADP
ijassa-497	65	4	anomaly	anomaly	NOUN
ijassa-497	65	5	detection	detection	NOUN
ijassa-497	65	6	are	be	AUX
ijassa-497	65	7	not	not	PART
ijassa-497	65	8	applicable	applicable	ADJ
ijassa-497	65	9	due	due	ADP
ijassa-497	65	10	to	to	ADP
ijassa-497	65	11	their	their	PRON
ijassa-497	65	12	computational	computational	ADJ
ijassa-497	65	13	complexity	complexity	NOUN
ijassa-497	65	14	and	and	CCONJ
ijassa-497	65	15	memory	memory	NOUN
ijassa-497	65	16	consumption	consumption	NOUN
ijassa-497	65	17	,	,	PUNCT
ijassa-497	65	18	since	since	SCONJ
ijassa-497	65	19	the	the	DET
ijassa-497	65	20	number	number	NOUN
ijassa-497	65	21	of	of	ADP
ijassa-497	65	22	elements	element	NOUN
ijassa-497	65	23	in	in	ADP
ijassa-497	65	24	the	the	DET
ijassa-497	65	25	set	set	NOUN
ijassa-497	65	26	is	be	AUX
ijassa-497	65	27	growing	grow	VERB
ijassa-497	65	28	and	and	CCONJ
ijassa-497	65	29	therefore	therefore	ADV
ijassa-497	65	30	it	it	PRON
ijassa-497	65	31	is	be	AUX
ijassa-497	65	32	not	not	PART
ijassa-497	65	33	possible	possible	ADJ
ijassa-497	65	34	to	to	PART
ijassa-497	65	35	store	store	VERB
ijassa-497	65	36	all	all	DET
ijassa-497	65	37	previously	previously	ADV
ijassa-497	65	38	observed	observe	VERB
ijassa-497	65	39	data	datum	NOUN
ijassa-497	65	40	.	.	PUNCT
ijassa-497	66	1	it	it	PRON
ijassa-497	66	2	is	be	AUX
ijassa-497	66	3	worth	worth	ADJ
ijassa-497	66	4	emphasizing	emphasize	VERB
ijassa-497	66	5	that	that	SCONJ
ijassa-497	66	6	frequently	frequently	ADV
ijassa-497	66	7	the	the	DET
ijassa-497	66	8	concept	concept	NOUN
ijassa-497	66	9	of	of	ADP
ijassa-497	66	10	anomaly	anomaly	NOUN
ijassa-497	66	11	could	could	AUX
ijassa-497	66	12	change	change	VERB
ijassa-497	66	13	in	in	ADP
ijassa-497	66	14	the	the	DET
ijassa-497	66	15	course	course	NOUN
ijassa-497	66	16	of	of	ADP
ijassa-497	66	17	time	time	NOUN
ijassa-497	66	18	.	.	PUNCT
ijassa-497	67	1	this	this	DET
ijassa-497	67	2	phenomenon	phenomenon	NOUN
ijassa-497	67	3	is	be	AUX
ijassa-497	67	4	called	call	VERB
ijassa-497	67	5	as	as	ADP
ijassa-497	67	6	concept	concept	NOUN
ijassa-497	67	7	drift	drift	NOUN
ijassa-497	67	8	.	.	PUNCT
ijassa-497	68	1	in	in	ADP
ijassa-497	68	2	general	general	ADJ
ijassa-497	68	3	,	,	PUNCT
ijassa-497	68	4	a	a	DET
ijassa-497	68	5	streaming	streaming	NOUN
ijassa-497	68	6	version	version	NOUN
ijassa-497	68	7	of	of	ADP
ijassa-497	68	8	an	an	DET
ijassa-497	68	9	anomaly	anomaly	NOUN
ijassa-497	68	10	detection	detection	NOUN
ijassa-497	68	11	algorithm	algorithm	NOUN
ijassa-497	68	12	should	should	AUX
ijassa-497	68	13	have	have	VERB
ijassa-497	68	14	an	an	DET
ijassa-497	68	15	ability	ability	NOUN
ijassa-497	68	16	of	of	ADP
ijassa-497	68	17	adaptation	adaptation	NOUN
ijassa-497	68	18	to	to	ADP
ijassa-497	68	19	a	a	DET
ijassa-497	68	20	concept	concept	NOUN
ijassa-497	68	21	drift	drift	NOUN
ijassa-497	68	22	;	;	PUNCT
ijassa-497	68	23	therefore	therefore	ADV
ijassa-497	68	24	,	,	PUNCT
ijassa-497	68	25	such	such	ADJ
ijassa-497	68	26	algorithms	algorithm	NOUN
ijassa-497	68	27	are	be	AUX
ijassa-497	68	28	usually	usually	ADV
ijassa-497	68	29	based	base	VERB
ijassa-497	68	30	on	on	ADP
ijassa-497	68	31	one	one	NUM
ijassa-497	68	32	of	of	ADP
ijassa-497	68	33	the	the	DET
ijassa-497	68	34	following	follow	VERB
ijassa-497	68	35	strategies	strategy	NOUN
ijassa-497	68	36	of	of	ADP
ijassa-497	68	37	accumulating	accumulate	VERB
ijassa-497	68	38	information	information	NOUN
ijassa-497	68	39	about	about	ADP
ijassa-497	68	40	current	current	ADJ
ijassa-497	68	41	changes	change	NOUN
ijassa-497	68	42	in	in	ADP
ijassa-497	68	43	data	datum	NOUN
ijassa-497	68	44	:	:	PUNCT
ijassa-497	68	45	namely	namely	ADV
ijassa-497	68	46	windowing	windowe	VERB
ijassa-497	68	47	and	and	CCONJ
ijassa-497	68	48	exponential	exponential	NOUN
ijassa-497	68	49	smoothing	smoothing	NOUN
ijassa-497	68	50	,	,	PUNCT
ijassa-497	68	51	which	which	PRON
ijassa-497	68	52	is	be	AUX
ijassa-497	68	53	also	also	ADV
ijassa-497	68	54	referred	refer	VERB
ijassa-497	68	55	to	to	ADP
ijassa-497	68	56	as	as	ADP
ijassa-497	68	57	decay	decay	NOUN
ijassa-497	68	58	.	.	PUNCT
ijassa-497	69	1	the	the	DET
ijassa-497	69	2	above	above	ADV
ijassa-497	69	3	-	-	PUNCT
ijassa-497	69	4	mentioned	mention	VERB
ijassa-497	69	5	issues	issue	NOUN
ijassa-497	69	6	are	be	AUX
ijassa-497	69	7	considered	consider	VERB
ijassa-497	69	8	in	in	ADP
ijassa-497	69	9	the	the	DET
ijassa-497	69	10	paper	paper	NOUN
ijassa-497	69	11	[	[	X
ijassa-497	69	12	14	14	NUM
ijassa-497	69	13	]	]	PUNCT
ijassa-497	69	14	.	.	PUNCT
ijassa-497	70	1	the	the	DET
ijassa-497	70	2	proposed	propose	VERB
ijassa-497	70	3	expose	expose	NOUN
ijassa-497	70	4	algorithm	algorithm	NOUN
ijassa-497	70	5	is	be	AUX
ijassa-497	70	6	developed	develop	VERB
ijassa-497	70	7	within	within	ADP
ijassa-497	70	8	the	the	DET
ijassa-497	70	9	framework	framework	NOUN
ijassa-497	70	10	of	of	ADP
ijassa-497	70	11	reproducing	reproduce	VERB
ijassa-497	70	12	kernel	kernel	PROPN
ijassa-497	70	13	hilbert	hilbert	PROPN
ijassa-497	70	14	space	space	NOUN
ijassa-497	70	15	(	(	PUNCT
ijassa-497	70	16	rkhs	rkh	NOUN
ijassa-497	70	17	)	)	PUNCT
ijassa-497	70	18	and	and	CCONJ
ijassa-497	70	19	exploits	exploit	VERB
ijassa-497	70	20	the	the	DET
ijassa-497	70	21	concept	concept	NOUN
ijassa-497	70	22	of	of	ADP
ijassa-497	70	23	kernel	kernel	PROPN
ijassa-497	70	24	mean	mean	VERB
ijassa-497	70	25	embedding	embed	VERB
ijassa-497	70	26	.	.	PUNCT
ijassa-497	71	1	in	in	ADP
ijassa-497	71	2	a	a	DET
ijassa-497	71	3	nutshell	nutshell	NOUN
ijassa-497	71	4	,	,	PUNCT
ijassa-497	71	5	the	the	DET
ijassa-497	71	6	estimator	estimator	NOUN
ijassa-497	71	7	used	use	VERB
ijassa-497	71	8	in	in	ADP
ijassa-497	71	9	the	the	DET
ijassa-497	71	10	algorithm	algorithm	NOUN
ijassa-497	71	11	could	could	AUX
ijassa-497	71	12	be	be	AUX
ijassa-497	71	13	described	describe	VERB
ijassa-497	71	14	as	as	ADP
ijassa-497	71	15	a	a	DET
ijassa-497	71	16	dot	dot	NOUN
ijassa-497	71	17	product	product	NOUN
ijassa-497	71	18	of	of	ADP
ijassa-497	71	19	a	a	DET
ijassa-497	71	20	kernel	kernel	NOUN
ijassa-497	71	21	mean	mean	NOUN
ijassa-497	71	22	map	map	NOUN
ijassa-497	71	23	and	and	CCONJ
ijassa-497	71	24	a	a	DET
ijassa-497	71	25	feature	feature	NOUN
ijassa-497	71	26	map	map	NOUN
ijassa-497	71	27	of	of	ADP
ijassa-497	71	28	the	the	DET
ijassa-497	71	29	observed	observe	VERB
ijassa-497	71	30	data	data	NOUN
ijassa-497	71	31	point	point	NOUN
ijassa-497	71	32	.	.	PUNCT
ijassa-497	72	1	however	however	ADV
ijassa-497	72	2	,	,	PUNCT
ijassa-497	72	3	the	the	DET
ijassa-497	72	4	produced	produce	VERB
ijassa-497	72	5	anomaly	anomaly	NOUN
ijassa-497	72	6	score	score	NOUN
ijassa-497	72	7	has	have	VERB
ijassa-497	72	8	a	a	DET
ijassa-497	72	9	lack	lack	NOUN
ijassa-497	72	10	of	of	ADP
ijassa-497	72	11	intuitive	intuitive	ADJ
ijassa-497	72	12	interpretability	interpretability	NOUN
ijassa-497	72	13	and	and	CCONJ
ijassa-497	72	14	therefore	therefore	ADV
ijassa-497	72	15	the	the	DET
ijassa-497	72	16	false	false	ADJ
ijassa-497	72	17	alarm	alarm	NOUN
ijassa-497	72	18	rate	rate	NOUN
ijassa-497	72	19	could	could	AUX
ijassa-497	72	20	not	not	PART
ijassa-497	72	21	be	be	AUX
ijassa-497	72	22	guaranteed	guarantee	VERB
ijassa-497	72	23	when	when	SCONJ
ijassa-497	72	24	anomaly	anomaly	NOUN
ijassa-497	72	25	threshold	threshold	NOUN
ijassa-497	72	26	is	be	AUX
ijassa-497	72	27	chosen	choose	VERB
ijassa-497	72	28	.	.	PUNCT
ijassa-497	73	1	in	in	ADP
ijassa-497	73	2	order	order	NOUN
ijassa-497	73	3	to	to	PART
ijassa-497	73	4	eliminate	eliminate	VERB
ijassa-497	73	5	this	this	DET
ijassa-497	73	6	drawback	drawback	NOUN
ijassa-497	73	7	we	we	PRON
ijassa-497	73	8	utilize	utilize	VERB
ijassa-497	73	9	lazy	lazy	ADJ
ijassa-497	73	10	drifting	drift	VERB
ijassa-497	73	11	conformal	conformal	ADJ
ijassa-497	73	12	detector	detector	NOUN
ijassa-497	73	13	procedure	procedure	NOUN
ijassa-497	73	14	proposed	propose	VERB
ijassa-497	73	15	in	in	ADP
ijassa-497	73	16	[	[	X
ijassa-497	73	17	32	32	NUM
ijassa-497	73	18	]	]	PUNCT
ijassa-497	73	19	to	to	PART
ijassa-497	73	20	construct	construct	VERB
ijassa-497	73	21	the	the	DET
ijassa-497	73	22	algorithm	algorithm	NOUN
ijassa-497	73	23	with	with	ADP
ijassa-497	73	24	probabilistic	probabilistic	ADJ
ijassa-497	73	25	interpretation	interpretation	NOUN
ijassa-497	73	26	of	of	ADP
ijassa-497	73	27	the	the	DET
ijassa-497	73	28	anomaly	anomaly	NOUN
ijassa-497	73	29	score	score	NOUN
ijassa-497	73	30	while	while	SCONJ
ijassa-497	73	31	based	base	VERB
ijassa-497	73	32	on	on	ADP
ijassa-497	73	33	the	the	DET
ijassa-497	73	34	idea	idea	NOUN
ijassa-497	73	35	of	of	ADP
ijassa-497	73	36	kernel	kernel	PROPN
ijassa-497	73	37	mean	mean	VERB
ijassa-497	73	38	embedding	embed	VERB
ijassa-497	73	39	proposed	propose	VERB
ijassa-497	73	40	in	in	ADP
ijassa-497	73	41	[	[	X
ijassa-497	73	42	14	14	NUM
ijassa-497	73	43	]	]	PUNCT
ijassa-497	73	44	.	.	PUNCT
ijassa-497	74	1	the	the	DET
ijassa-497	74	2	structure	structure	NOUN
ijassa-497	74	3	of	of	ADP
ijassa-497	74	4	the	the	DET
ijassa-497	74	5	paper	paper	NOUN
ijassa-497	74	6	is	be	AUX
ijassa-497	74	7	the	the	DET
ijassa-497	74	8	following	following	NOUN
ijassa-497	74	9	.	.	PUNCT
ijassa-497	75	1	sections	section	NOUN
ijassa-497	75	2	2	2	NUM
ijassa-497	75	3	and	and	CCONJ
ijassa-497	75	4	3	3	NUM
ijassa-497	75	5	shed	shed	VERB
ijassa-497	75	6	light	light	NOUN
ijassa-497	75	7	on	on	ADP
ijassa-497	75	8	kernels	kernel	NOUN
ijassa-497	75	9	and	and	CCONJ
ijassa-497	75	10	conformal	conformal	ADJ
ijassa-497	75	11	anomaly	anomaly	NOUN
ijassa-497	75	12	detection	detection	NOUN
ijassa-497	75	13	respectively	respectively	ADV
ijassa-497	75	14	.	.	PUNCT
ijassa-497	76	1	the	the	DET
ijassa-497	76	2	proposed	propose	VERB
ijassa-497	76	3	algorithm	algorithm	NOUN
ijassa-497	76	4	is	be	AUX
ijassa-497	76	5	described	describe	VERB
ijassa-497	76	6	in	in	ADP
ijassa-497	76	7	section	section	NOUN
ijassa-497	76	8	4	4	NUM
ijassa-497	76	9	.	.	PUNCT
ijassa-497	77	1	in	in	ADP
ijassa-497	77	2	section	section	NOUN
ijassa-497	77	3	5	5	NUM
ijassa-497	77	4	,	,	PUNCT
ijassa-497	77	5	the	the	DET
ijassa-497	77	6	results	result	NOUN
ijassa-497	77	7	of	of	ADP
ijassa-497	77	8	empirical	empirical	ADJ
ijassa-497	77	9	evaluation	evaluation	NOUN
ijassa-497	77	10	(	(	PUNCT
ijassa-497	77	11	using	use	VERB
ijassa-497	77	12	numenta	numenta	PROPN
ijassa-497	77	13	anomaly	anomaly	NOUN
ijassa-497	77	14	benchmark	benchmark	NOUN
ijassa-497	77	15	)	)	PUNCT
ijassa-497	77	16	of	of	ADP
ijassa-497	77	17	the	the	DET
ijassa-497	77	18	proposed	propose	VERB
ijassa-497	77	19	algorithm	algorithm	NOUN
ijassa-497	77	20	are	be	AUX
ijassa-497	77	21	outlined	outline	VERB
ijassa-497	77	22	.	.	PUNCT
ijassa-497	78	1	finally	finally	ADV
ijassa-497	78	2	,	,	PUNCT
ijassa-497	78	3	section	section	NOUN
ijassa-497	78	4	6	6	NUM
ijassa-497	78	5	describes	describe	NOUN
ijassa-497	78	6	achieved	achieve	VERB
ijassa-497	78	7	results	result	NOUN
ijassa-497	78	8	and	and	CCONJ
ijassa-497	78	9	the	the	DET
ijassa-497	78	10	work	work	NOUN
ijassa-497	78	11	still	still	ADV
ijassa-497	78	12	to	to	PART
ijassa-497	78	13	be	be	AUX
ijassa-497	78	14	done	do	VERB
ijassa-497	78	15	.	.	PUNCT
ijassa-497	79	1	2	2	X
ijassa-497	79	2	.	.	X
ijassa-497	79	3	overview	overview	NOUN
ijassa-497	79	4	of	of	ADP
ijassa-497	79	5	kernel	kernel	NOUN
ijassa-497	79	6	-	-	PUNCT
ijassa-497	79	7	based	base	VERB
ijassa-497	79	8	methods	method	NOUN
ijassa-497	79	9	for	for	ADP
ijassa-497	79	10	anomaly	anomaly	NOUN
ijassa-497	79	11	detection	detection	NOUN
ijassa-497	79	12	in	in	ADP
ijassa-497	79	13	machine	machine	NOUN
ijassa-497	79	14	learning	learn	VERB
ijassa-497	79	15	kernels	kernel	NOUN
ijassa-497	79	16	are	be	AUX
ijassa-497	79	17	broadly	broadly	ADV
ijassa-497	79	18	used	use	VERB
ijassa-497	79	19	for	for	ADP
ijassa-497	79	20	handling	handle	VERB
ijassa-497	79	21	data	datum	NOUN
ijassa-497	79	22	of	of	ADP
ijassa-497	79	23	diverse	diverse	ADJ
ijassa-497	79	24	nature	nature	NOUN
ijassa-497	79	25	.	.	PUNCT
ijassa-497	80	1	therefore	therefore	ADV
ijassa-497	80	2	it	it	PRON
ijassa-497	80	3	is	be	AUX
ijassa-497	80	4	not	not	PART
ijassa-497	80	5	surprising	surprising	ADJ
ijassa-497	80	6	that	that	SCONJ
ijassa-497	80	7	a	a	DET
ijassa-497	80	8	number	number	NOUN
ijassa-497	80	9	of	of	ADP
ijassa-497	80	10	anomaly	anomaly	NOUN
ijassa-497	80	11	detection	detection	NOUN
ijassa-497	80	12	methods	method	NOUN
ijassa-497	80	13	are	be	AUX
ijassa-497	80	14	based	base	VERB
ijassa-497	80	15	on	on	ADP
ijassa-497	80	16	the	the	DET
ijassa-497	80	17	kernel	kernel	PROPN
ijassa-497	80	18	framework	framework	NOUN
ijassa-497	80	19	.	.	PUNCT
ijassa-497	81	1	in	in	ADP
ijassa-497	81	2	this	this	DET
ijassa-497	81	3	section	section	NOUN
ijassa-497	81	4	we	we	PRON
ijassa-497	81	5	give	give	VERB
ijassa-497	81	6	some	some	DET
ijassa-497	81	7	necessary	necessary	ADJ
ijassa-497	81	8	definitions	definition	NOUN
ijassa-497	81	9	and	and	CCONJ
ijassa-497	81	10	provide	provide	VERB
ijassa-497	81	11	an	an	DET
ijassa-497	81	12	overview	overview	NOUN
ijassa-497	81	13	of	of	ADP
ijassa-497	81	14	such	such	ADJ
ijassa-497	81	15	anomaly	anomaly	NOUN
ijassa-497	81	16	detection	detection	NOUN
ijassa-497	81	17	methods	method	NOUN
ijassa-497	81	18	which	which	PRON
ijassa-497	81	19	uses	use	VERB
ijassa-497	81	20	kernels	kernel	NOUN
ijassa-497	81	21	and	and	CCONJ
ijassa-497	81	22	therefore	therefore	ADV
ijassa-497	81	23	are	be	AUX
ijassa-497	81	24	applicable	applicable	ADJ
ijassa-497	81	25	to	to	ADP
ijassa-497	81	26	data	datum	NOUN
ijassa-497	81	27	of	of	ADP
ijassa-497	81	28	various	various	ADJ
ijassa-497	81	29	types	type	NOUN
ijassa-497	81	30	.	.	PUNCT
ijassa-497	82	1	2.1	2.1	NUM
ijassa-497	82	2	.	.	PUNCT
ijassa-497	82	3	introduction	introduction	NOUN
ijassa-497	82	4	to	to	ADP
ijassa-497	82	5	kernels	kernels	PROPN
ijassa-497	82	6	reproducing	reproduce	VERB
ijassa-497	82	7	kernel	kernel	PROPN
ijassa-497	82	8	hilbert	hilbert	PROPN
ijassa-497	82	9	space	space	NOUN
ijassa-497	82	10	(	(	PUNCT
ijassa-497	82	11	rkhs	rkh	NOUN
ijassa-497	82	12	)	)	PUNCT
ijassa-497	82	13	is	be	AUX
ijassa-497	82	14	a	a	DET
ijassa-497	82	15	hilbert	hilbert	NOUN
ijassa-497	82	16	space	space	NOUN
ijassa-497	82	17	(	(	PUNCT
ijassa-497	82	18	h	h	NOUN
ijassa-497	82	19	,	,	PUNCT
ijassa-497	82	20	〈	〈	PROPN
ijassa-497	82	21	·	·	SYM
ijassa-497	82	22	,	,	PUNCT
ijassa-497	82	23	·	·	SYM
ijassa-497	82	24	〉	〉	NUM
ijassa-497	82	25	)	)	PUNCT
ijassa-497	82	26	of	of	ADP
ijassa-497	82	27	functions	function	NOUN
ijassa-497	82	28	f	f	X
ijassa-497	82	29	:	:	PUNCT
ijassa-497	83	1	x	x	X
ijassa-497	84	1	→	→	SYM
ijassa-497	84	2	r	r	NOUN
ijassa-497	84	3	if	if	SCONJ
ijassa-497	84	4	the	the	DET
ijassa-497	84	5	evaluational	evaluational	ADJ
ijassa-497	84	6	functional	functional	ADJ
ijassa-497	84	7	δ̄x	δ̄x	NOUN
ijassa-497	84	8	:	:	PUNCT
ijassa-497	84	9	f	f	X
ijassa-497	84	10	→	→	SYM
ijassa-497	84	11	f(x	f(x	PROPN
ijassa-497	84	12	)	)	PUNCT
ijassa-497	84	13	is	be	AUX
ijassa-497	84	14	continuous	continuous	ADJ
ijassa-497	84	15	.	.	PUNCT
ijassa-497	85	1	copyright	copyright	NOUN
ijassa-497	85	2	©	©	PROPN
ijassa-497	85	3	2017	2017	NUM
ijassa-497	85	4	assa	assa	NOUN
ijassa-497	85	5	.	.	PUNCT
ijassa-497	86	1	adv	adv	PROPN
ijassa-497	86	2	syst	syst	PROPN
ijassa-497	86	3	sci	sci	PROPN
ijassa-497	86	4	appl	appl	PROPN
ijassa-497	86	5	(	(	PUNCT
ijassa-497	86	6	2017	2017	NUM
ijassa-497	86	7	)	)	PUNCT
ijassa-497	86	8	conformal	conformal	ADJ
ijassa-497	86	9	kernel	kernel	NOUN
ijassa-497	86	10	expected	expect	VERB
ijassa-497	86	11	similarity	similarity	NOUN
ijassa-497	86	12	for	for	ADP
ijassa-497	86	13	anomaly	anomaly	NOUN
ijassa-497	86	14	detection	detection	NOUN
ijassa-497	86	15	in	in	ADP
ijassa-497	86	16	time	time	NOUN
ijassa-497	86	17	-	-	PUNCT
ijassa-497	86	18	series	series	NOUN
ijassa-497	86	19	data	datum	NOUN
ijassa-497	86	20	25	25	NUM
ijassa-497	86	21	reproducing	reproducing	NOUN
ijassa-497	86	22	kernel	kernel	NOUN
ijassa-497	86	23	of	of	ADP
ijassa-497	86	24	h	h	PROPN
ijassa-497	86	25	is	be	AUX
ijassa-497	86	26	a	a	DET
ijassa-497	86	27	function	function	NOUN
ijassa-497	86	28	k	k	NOUN
ijassa-497	86	29	:	:	PUNCT
ijassa-497	86	30	x	x	SYM
ijassa-497	86	31	×	×	NOUN
ijassa-497	86	32	x	x	INTJ
ijassa-497	86	33	→	→	SYM
ijassa-497	86	34	r	r	NOUN
ijassa-497	86	35	which	which	PRON
ijassa-497	86	36	satisfies	satisfy	VERB
ijassa-497	86	37	the	the	DET
ijassa-497	86	38	reproducing	reproducing	NOUN
ijassa-497	86	39	property	property	NOUN
ijassa-497	86	40	:	:	PUNCT
ijassa-497	86	41	〈	〈	PROPN
ijassa-497	86	42	f	f	PROPN
ijassa-497	86	43	,	,	PUNCT
ijassa-497	86	44	k(x	k(x	PROPN
ijassa-497	86	45	,	,	PUNCT
ijassa-497	86	46	·	·	PUNCT
ijassa-497	86	47	)	)	PUNCT
ijassa-497	86	48	〉	〉	PROPN
ijassa-497	86	49	=	=	SYM
ijassa-497	86	50	f(x	f(x	PROPN
ijassa-497	86	51	)	)	PUNCT
ijassa-497	86	52	,	,	PUNCT
ijassa-497	86	53	〈	〈	PROPN
ijassa-497	86	54	k(x	k(x	PROPN
ijassa-497	86	55	,	,	PUNCT
ijassa-497	86	56	·	·	PUNCT
ijassa-497	86	57	)	)	PUNCT
ijassa-497	86	58	,	,	PUNCT
ijassa-497	86	59	k(y	k(y	PROPN
ijassa-497	86	60	,	,	PUNCT
ijassa-497	86	61	·	·	PUNCT
ijassa-497	86	62	)	)	PUNCT
ijassa-497	86	63	〉	〉	NOUN
ijassa-497	87	1	=	=	SYM
ijassa-497	87	2	k(x	k(x	PROPN
ijassa-497	87	3	,	,	PUNCT
ijassa-497	87	4	y	y	PROPN
ijassa-497	87	5	)	)	PUNCT
ijassa-497	87	6	.	.	PUNCT
ijassa-497	88	1	the	the	DET
ijassa-497	88	2	map	map	NOUN
ijassa-497	88	3	φ	φ	X
ijassa-497	88	4	:	:	PUNCT
ijassa-497	88	5	x	x	SYM
ijassa-497	88	6	→	→	SYM
ijassa-497	88	7	h	h	NOUN
ijassa-497	88	8	with	with	ADP
ijassa-497	88	9	the	the	DET
ijassa-497	88	10	property	property	NOUN
ijassa-497	88	11	that	that	PRON
ijassa-497	88	12	k(x	k(x	PROPN
ijassa-497	88	13	,	,	PUNCT
ijassa-497	88	14	y	y	PROPN
ijassa-497	88	15	)	)	PUNCT
ijassa-497	88	16	=	=	PUNCT
ijassa-497	89	1	〈	〈	PROPN
ijassa-497	89	2	φ(x	φ(x	PROPN
ijassa-497	89	3	)	)	PUNCT
ijassa-497	89	4	,	,	PUNCT
ijassa-497	89	5	φ(y	φ(y	NOUN
ijassa-497	89	6	)	)	PUNCT
ijassa-497	89	7	〉	〉	PROPN
ijassa-497	89	8	is	be	AUX
ijassa-497	89	9	referred	refer	VERB
ijassa-497	89	10	to	to	ADP
ijassa-497	89	11	as	as	ADP
ijassa-497	89	12	a	a	DET
ijassa-497	89	13	feature	feature	NOUN
ijassa-497	89	14	map	map	NOUN
ijassa-497	89	15	.	.	PUNCT
ijassa-497	90	1	definition	definition	NOUN
ijassa-497	90	2	2.1	2.1	NUM
ijassa-497	90	3	(	(	PUNCT
ijassa-497	90	4	expected	expect	VERB
ijassa-497	90	5	similarity	similarity	NOUN
ijassa-497	90	6	estimation	estimation	NOUN
ijassa-497	90	7	):	):	PUNCT
ijassa-497	90	8	the	the	DET
ijassa-497	90	9	expected	expect	VERB
ijassa-497	90	10	similarity	similarity	NOUN
ijassa-497	90	11	[	[	X
ijassa-497	90	12	14	14	NUM
ijassa-497	90	13	]	]	PUNCT
ijassa-497	90	14	of	of	ADP
ijassa-497	90	15	z	z	PROPN
ijassa-497	90	16	∈	∈	PROPN
ijassa-497	90	17	x	x	PUNCT
ijassa-497	90	18	given	give	VERB
ijassa-497	90	19	the	the	DET
ijassa-497	90	20	probability	probability	NOUN
ijassa-497	90	21	distribution	distribution	NOUN
ijassa-497	90	22	p(x	p(x	NOUN
ijassa-497	90	23	)	)	PUNCT
ijassa-497	90	24	is	be	AUX
ijassa-497	90	25	defined	define	VERB
ijassa-497	90	26	as	as	ADP
ijassa-497	90	27	:	:	PUNCT
ijassa-497	90	28	η(z	η(z	PROPN
ijassa-497	90	29	)	)	PUNCT
ijassa-497	91	1	=	=	PUNCT
ijassa-497	91	2	ex	ex	X
ijassa-497	92	1	[	[	X
ijassa-497	92	2	φ(z	φ(z	NOUN
ijassa-497	92	3	)	)	PUNCT
ijassa-497	92	4	]	]	PUNCT
ijassa-497	93	1	=	=	PUNCT
ijassa-497	93	2	∫	∫	PROPN
ijassa-497	93	3	x	x	PUNCT
ijassa-497	93	4	k(z	k(z	PROPN
ijassa-497	93	5	,	,	PUNCT
ijassa-497	93	6	x)dp(x	x)dp(x	PROPN
ijassa-497	93	7	)	)	PUNCT
ijassa-497	93	8	.	.	PUNCT
ijassa-497	94	1	definition	definition	NOUN
ijassa-497	94	2	2.2	2.2	NUM
ijassa-497	94	3	(	(	PUNCT
ijassa-497	94	4	kernel	kernel	PROPN
ijassa-497	94	5	embedding	embed	VERB
ijassa-497	94	6	):	):	PUNCT
ijassa-497	94	7	kernel	kernel	NOUN
ijassa-497	94	8	embedding	embed	VERB
ijassa-497	94	9	of	of	ADP
ijassa-497	94	10	the	the	DET
ijassa-497	94	11	distribution	distribution	NOUN
ijassa-497	94	12	p	p	NOUN
ijassa-497	94	13	has	have	VERB
ijassa-497	94	14	the	the	DET
ijassa-497	94	15	form	form	NOUN
ijassa-497	94	16	µ[p	µ[p	VERB
ijassa-497	94	17	]	]	X
ijassa-497	95	1	=	=	SYM
ijassa-497	95	2	∫	∫	PROPN
ijassa-497	95	3	x	x	SYM
ijassa-497	95	4	k(x	k(x	PROPN
ijassa-497	95	5	,	,	PUNCT
ijassa-497	95	6	·	·	PUNCT
ijassa-497	95	7	)	)	PUNCT
ijassa-497	95	8	dp(x	dp(x	PROPN
ijassa-497	95	9	)	)	PUNCT
ijassa-497	95	10	.	.	PUNCT
ijassa-497	96	1	expectation	expectation	NOUN
ijassa-497	96	2	of	of	ADP
ijassa-497	96	3	any	any	DET
ijassa-497	96	4	f	f	PROPN
ijassa-497	96	5	∈	∈	PROPN
ijassa-497	96	6	h	h	NOUN
ijassa-497	96	7	ex	ex	X
ijassa-497	97	1	[	[	X
ijassa-497	97	2	f	f	X
ijassa-497	97	3	]	]	X
ijassa-497	97	4	=	=	PUNCT
ijassa-497	97	5	〈	〈	PROPN
ijassa-497	97	6	f	f	PROPN
ijassa-497	97	7	,	,	PUNCT
ijassa-497	97	8	µ[p]〉h	µ[p]〉h	PROPN
ijassa-497	97	9	.	.	PUNCT
ijassa-497	98	1	thus	thus	ADV
ijassa-497	98	2	,	,	PUNCT
ijassa-497	98	3	η(z	η(z	PROPN
ijassa-497	98	4	)	)	PUNCT
ijassa-497	98	5	=	=	PUNCT
ijassa-497	99	1	〈	〈	PROPN
ijassa-497	99	2	φ(z	φ(z	PROPN
ijassa-497	99	3	)	)	PUNCT
ijassa-497	99	4	,	,	PUNCT
ijassa-497	99	5	µ[p]〉h	µ[p]〉h	NOUN
ijassa-497	99	6	.	.	PUNCT
ijassa-497	100	1	given	give	VERB
ijassa-497	100	2	the	the	DET
ijassa-497	100	3	empirical	empirical	ADJ
ijassa-497	100	4	distribution	distribution	NOUN
ijassa-497	100	5	pn(x	pn(x	PUNCT
ijassa-497	100	6	)	)	PUNCT
ijassa-497	100	7	by	by	ADP
ijassa-497	100	8	observing	observe	VERB
ijassa-497	100	9	n	n	PRON
ijassa-497	100	10	realizations	realization	NOUN
ijassa-497	100	11	{	{	PUNCT
ijassa-497	100	12	x1	x1	PROPN
ijassa-497	100	13	,	,	PUNCT
ijassa-497	100	14	.	.	PUNCT
ijassa-497	100	15	.	.	PUNCT
ijassa-497	100	16	.	.	PUNCT
ijassa-497	101	1	xn	xn	X
ijassa-497	101	2	}	}	PUNCT
ijassa-497	101	3	independently	independently	ADV
ijassa-497	101	4	sampled	sample	VERB
ijassa-497	101	5	from	from	ADP
ijassa-497	101	6	p	p	PRON
ijassa-497	101	7	,	,	PUNCT
ijassa-497	101	8	one	one	PRON
ijassa-497	101	9	could	could	AUX
ijassa-497	101	10	approximate	approximate	VERB
ijassa-497	101	11	µ[p	µ[p	ADV
ijassa-497	101	12	]	]	PUNCT
ijassa-497	101	13	as	as	SCONJ
ijassa-497	101	14	follows	follow	VERB
ijassa-497	101	15	:	:	PUNCT
ijassa-497	101	16	µ[p	µ[p	ADV
ijassa-497	101	17	]	]	X
ijassa-497	102	1	≈	≈	PROPN
ijassa-497	102	2	µ[pn	µ[pn	PROPN
ijassa-497	102	3	]	]	X
ijassa-497	102	4	=	=	SYM
ijassa-497	102	5	1	1	NUM
ijassa-497	102	6	n	n	NUM
ijassa-497	102	7	n∑	n∑	NOUN
ijassa-497	102	8	i=1	i=1	PROPN
ijassa-497	102	9	φ(xi	φ(xi	PROPN
ijassa-497	102	10	)	)	PUNCT
ijassa-497	102	11	.	.	PUNCT
ijassa-497	103	1	this	this	DET
ijassa-497	103	2	approach	approach	NOUN
ijassa-497	103	3	is	be	AUX
ijassa-497	103	4	referred	refer	VERB
ijassa-497	103	5	to	to	ADP
ijassa-497	103	6	as	as	ADP
ijassa-497	103	7	empirical	empirical	ADJ
ijassa-497	103	8	kernel	kernel	NOUN
ijassa-497	103	9	embedding	embed	VERB
ijassa-497	103	10	[	[	X
ijassa-497	103	11	29	29	NUM
ijassa-497	103	12	]	]	PUNCT
ijassa-497	103	13	and	and	CCONJ
ijassa-497	103	14	given	give	VERB
ijassa-497	103	15	that	that	PRON
ijassa-497	103	16	‖φ(x)‖	‖φ(x)‖	ADJ
ijassa-497	103	17	≤	≤	NUM
ijassa-497	103	18	c	c	X
ijassa-497	103	19	,	,	PUNCT
ijassa-497	103	20	c	c	X
ijassa-497	103	21	>	>	X
ijassa-497	103	22	0	0	PUNCT
ijassa-497	104	1	the	the	DET
ijassa-497	104	2	following	follow	VERB
ijassa-497	104	3	guarantee	guarantee	NOUN
ijassa-497	104	4	has	have	AUX
ijassa-497	104	5	been	be	AUX
ijassa-497	104	6	proved	prove	VERB
ijassa-497	104	7	by	by	ADP
ijassa-497	104	8	schneider	schneider	NOUN
ijassa-497	105	1	[	[	X
ijassa-497	105	2	30	30	NUM
ijassa-497	105	3	]	]	PUNCT
ijassa-497	105	4	for	for	ADP
ijassa-497	105	5	all	all	DET
ijassa-497	105	6	ε	ε	PROPN
ijassa-497	105	7	>	>	X
ijassa-497	105	8	0	0	NUM
ijassa-497	105	9	:	:	PUNCT
ijassa-497	105	10	p	p	X
ijassa-497	105	11	(	(	PUNCT
ijassa-497	105	12	‖µ[p]−	‖µ[p]−	X
ijassa-497	105	13	µ[pn]‖	µ[pn]‖	PROPN
ijassa-497	105	14	≥	≥	X
ijassa-497	105	15	ε	ε	PROPN
ijassa-497	105	16	)	)	PUNCT
ijassa-497	105	17	≤	≤	NOUN
ijassa-497	106	1	2e−	2e−	PROPN
ijassa-497	106	2	nε2	nε2	PROPN
ijassa-497	106	3	8c2	8c2	NUM
ijassa-497	106	4	.	.	PUNCT
ijassa-497	107	1	considering	consider	VERB
ijassa-497	107	2	above	above	ADP
ijassa-497	107	3	mentioned	mention	VERB
ijassa-497	107	4	,	,	PUNCT
ijassa-497	107	5	having	having	AUX
ijassa-497	107	6	observed	observe	VERB
ijassa-497	107	7	{	{	PUNCT
ijassa-497	107	8	x1	x1	PROPN
ijassa-497	107	9	,	,	PUNCT
ijassa-497	107	10	.	.	PUNCT
ijassa-497	107	11	.	.	PUNCT
ijassa-497	107	12	.	.	PUNCT
ijassa-497	108	1	xn	xn	PUNCT
ijassa-497	108	2	}	}	PUNCT
ijassa-497	108	3	,	,	PUNCT
ijassa-497	108	4	the	the	DET
ijassa-497	108	5	expected	expect	VERB
ijassa-497	108	6	similarity	similarity	NOUN
ijassa-497	108	7	estimation	estimation	NOUN
ijassa-497	108	8	for	for	ADP
ijassa-497	108	9	z	z	PROPN
ijassa-497	108	10	∈	∈	PROPN
ijassa-497	108	11	x	x	X
ijassa-497	108	12	is	be	AUX
ijassa-497	108	13	η(z	η(z	PROPN
ijassa-497	108	14	)	)	PUNCT
ijassa-497	108	15	=	=	PUNCT
ijassa-497	109	1	〈	〈	PROPN
ijassa-497	109	2	φ(z	φ(z	PROPN
ijassa-497	109	3	)	)	PUNCT
ijassa-497	109	4	,	,	PUNCT
ijassa-497	110	1	µ[p]〉h	µ[p]〉h	NOUN
ijassa-497	110	2	≈	≈	PROPN
ijassa-497	110	3	〈	〈	PROPN
ijassa-497	110	4	φ(z	φ(z	PROPN
ijassa-497	110	5	)	)	PUNCT
ijassa-497	110	6	,	,	PUNCT
ijassa-497	110	7	µ[pn]〉h	µ[pn]〉h	PUNCT
ijassa-497	110	8	=	=	SYM
ijassa-497	110	9	1	1	NUM
ijassa-497	110	10	n	n	NUM
ijassa-497	110	11	n∑	n∑	NOUN
ijassa-497	110	12	i=1	i=1	PROPN
ijassa-497	111	1	k(z	k(z	PROPN
ijassa-497	111	2	,	,	PUNCT
ijassa-497	111	3	xi	xi	PROPN
ijassa-497	111	4	)	)	PUNCT
ijassa-497	111	5	.	.	PUNCT
ijassa-497	112	1	2.2	2.2	NUM
ijassa-497	112	2	.	.	PUNCT
ijassa-497	112	3	expose	expose	VERB
ijassa-497	112	4	expected	expect	VERB
ijassa-497	112	5	similarity	similarity	NOUN
ijassa-497	112	6	estimation	estimation	NOUN
ijassa-497	112	7	(	(	PUNCT
ijassa-497	112	8	expose	expose	VERB
ijassa-497	112	9	)	)	PUNCT
ijassa-497	112	10	that	that	PRON
ijassa-497	112	11	was	be	AUX
ijassa-497	112	12	proposed	propose	VERB
ijassa-497	112	13	in	in	ADP
ijassa-497	112	14	[	[	X
ijassa-497	112	15	14	14	NUM
ijassa-497	112	16	]	]	PUNCT
ijassa-497	112	17	is	be	AUX
ijassa-497	112	18	the	the	DET
ijassa-497	112	19	method	method	NOUN
ijassa-497	112	20	for	for	ADP
ijassa-497	112	21	anomaly	anomaly	NOUN
ijassa-497	112	22	detection	detection	NOUN
ijassa-497	112	23	which	which	PRON
ijassa-497	112	24	could	could	AUX
ijassa-497	112	25	handle	handle	VERB
ijassa-497	112	26	data	data	NOUN
ijassa-497	112	27	streams	stream	NOUN
ijassa-497	112	28	.	.	PUNCT
ijassa-497	113	1	for	for	ADP
ijassa-497	113	2	every	every	DET
ijassa-497	113	3	new	new	ADJ
ijassa-497	113	4	observation	observation	NOUN
ijassa-497	113	5	z	z	NOUN
ijassa-497	113	6	the	the	DET
ijassa-497	113	7	algorithm	algorithm	NOUN
ijassa-497	113	8	computes	compute	VERB
ijassa-497	113	9	an	an	DET
ijassa-497	113	10	anomaly	anomaly	NOUN
ijassa-497	113	11	score	score	NOUN
ijassa-497	113	12	η(z	η(z	PROPN
ijassa-497	113	13	)	)	PUNCT
ijassa-497	113	14	based	base	VERB
ijassa-497	113	15	on	on	ADP
ijassa-497	113	16	computed	compute	VERB
ijassa-497	113	17	empirical	empirical	ADJ
ijassa-497	113	18	kernel	kernel	NOUN
ijassa-497	113	19	mean	mean	NOUN
ijassa-497	113	20	map	map	NOUN
ijassa-497	113	21	wt	wt	INTJ
ijassa-497	113	22	of	of	ADP
ijassa-497	113	23	previously	previously	ADV
ijassa-497	113	24	observed	observe	VERB
ijassa-497	113	25	items	item	NOUN
ijassa-497	113	26	η(z	η(z	PROPN
ijassa-497	113	27	)	)	PUNCT
ijassa-497	114	1	=	=	PUNCT
ijassa-497	115	1	〈	〈	PROPN
ijassa-497	115	2	φ(z	φ(z	PROPN
ijassa-497	115	3	)	)	PUNCT
ijassa-497	115	4	,	,	PUNCT
ijassa-497	115	5	wt	wt	PROPN
ijassa-497	115	6	〉	〉	PROPN
ijassa-497	115	7	‖wt‖2	‖wt‖2	PROPN
ijassa-497	115	8	.	.	PUNCT
ijassa-497	116	1	copyright	copyright	NOUN
ijassa-497	116	2	©	©	PROPN
ijassa-497	116	3	2017	2017	NUM
ijassa-497	116	4	assa	assa	NOUN
ijassa-497	116	5	.	.	PUNCT
ijassa-497	117	1	adv	adv	PROPN
ijassa-497	117	2	syst	syst	PROPN
ijassa-497	117	3	sci	sci	PROPN
ijassa-497	117	4	appl	appl	PROPN
ijassa-497	117	5	(	(	PUNCT
ijassa-497	117	6	2017	2017	NUM
ijassa-497	117	7	)	)	PUNCT
ijassa-497	117	8	26	26	NUM
ijassa-497	117	9	aleksandr	aleksandr	PROPN
ijassa-497	117	10	safin	safin	PROPN
ijassa-497	117	11	,	,	PUNCT
ijassa-497	117	12	evgeny	evgeny	PROPN
ijassa-497	117	13	burnaev	burnaev	PROPN
ijassa-497	117	14	kernel	kernel	PROPN
ijassa-497	117	15	mean	mean	PROPN
ijassa-497	117	16	map	map	NOUN
ijassa-497	117	17	could	could	AUX
ijassa-497	117	18	be	be	AUX
ijassa-497	117	19	evaluated	evaluate	VERB
ijassa-497	117	20	using	use	VERB
ijassa-497	117	21	one	one	NUM
ijassa-497	117	22	of	of	ADP
ijassa-497	117	23	the	the	DET
ijassa-497	117	24	following	follow	VERB
ijassa-497	117	25	strategies	strategy	NOUN
ijassa-497	117	26	.	.	PUNCT
ijassa-497	118	1	the	the	DET
ijassa-497	118	2	first	first	ADJ
ijassa-497	118	3	strategy	strategy	NOUN
ijassa-497	118	4	is	be	AUX
ijassa-497	118	5	to	to	PART
ijassa-497	118	6	use	use	VERB
ijassa-497	118	7	a	a	DET
ijassa-497	118	8	sliding	slide	VERB
ijassa-497	118	9	window	window	NOUN
ijassa-497	118	10	of	of	ADP
ijassa-497	118	11	length	length	NOUN
ijassa-497	118	12	l	l	NOUN
ijassa-497	118	13	:	:	PUNCT
ijassa-497	118	14	wt	wt	NOUN
ijassa-497	118	15	=	=	SYM
ijassa-497	118	16	1	1	NUM
ijassa-497	118	17	l	l	NOUN
ijassa-497	118	18	t∑	t∑	X
ijassa-497	118	19	i	i	PRON
ijassa-497	118	20	=	=	NOUN
ijassa-497	118	21	t−l+1	t−l+1	PROPN
ijassa-497	118	22	φ(xi	φ(xi	NUM
ijassa-497	118	23	)	)	PUNCT
ijassa-497	118	24	.	.	PUNCT
ijassa-497	119	1	more	more	ADV
ijassa-497	119	2	flexible	flexible	ADJ
ijassa-497	119	3	approach	approach	NOUN
ijassa-497	119	4	is	be	AUX
ijassa-497	119	5	to	to	PART
ijassa-497	119	6	apply	apply	VERB
ijassa-497	119	7	exponential	exponential	NOUN
ijassa-497	119	8	smoothing	smoothing	NOUN
ijassa-497	119	9	to	to	ADP
ijassa-497	119	10	all	all	DET
ijassa-497	119	11	previous	previous	ADJ
ijassa-497	119	12	observations	observation	NOUN
ijassa-497	119	13	:	:	PUNCT
ijassa-497	119	14	wt	wt	PROPN
ijassa-497	119	15	=	=	PUNCT
ijassa-497	119	16	γφ(xt	γφ(xt	NOUN
ijassa-497	119	17	)	)	PUNCT
ijassa-497	120	1	+	+	CCONJ
ijassa-497	120	2	(	(	PUNCT
ijassa-497	120	3	1−	1−	NUM
ijassa-497	120	4	γ)wt−1	γ)wt−1	PROPN
ijassa-497	120	5	,	,	PUNCT
ijassa-497	120	6	t	t	X
ijassa-497	120	7	>	>	X
ijassa-497	121	1	1	1	NUM
ijassa-497	121	2	.	.	PUNCT
ijassa-497	122	1	the	the	DET
ijassa-497	122	2	parameter	parameter	NOUN
ijassa-497	122	3	γ	γ	PROPN
ijassa-497	122	4	reflects	reflect	VERB
ijassa-497	122	5	the	the	DET
ijassa-497	122	6	influence	influence	NOUN
ijassa-497	122	7	of	of	ADP
ijassa-497	122	8	a	a	DET
ijassa-497	122	9	new	new	ADJ
ijassa-497	122	10	data	data	NOUN
ijassa-497	122	11	item	item	NOUN
ijassa-497	122	12	.	.	PUNCT
ijassa-497	123	1	however	however	ADV
ijassa-497	123	2	,	,	PUNCT
ijassa-497	123	3	as	as	SCONJ
ijassa-497	123	4	already	already	ADV
ijassa-497	123	5	was	be	AUX
ijassa-497	123	6	indicated	indicate	VERB
ijassa-497	123	7	,	,	PUNCT
ijassa-497	123	8	the	the	DET
ijassa-497	123	9	anomaly	anomaly	NOUN
ijassa-497	123	10	score	score	NOUN
ijassa-497	123	11	provided	provide	VERB
ijassa-497	123	12	by	by	ADP
ijassa-497	123	13	this	this	DET
ijassa-497	123	14	algorithm	algorithm	NOUN
ijassa-497	123	15	could	could	AUX
ijassa-497	123	16	not	not	PART
ijassa-497	123	17	be	be	AUX
ijassa-497	123	18	interpreted	interpret	VERB
ijassa-497	123	19	in	in	ADP
ijassa-497	123	20	a	a	DET
ijassa-497	123	21	probabilistic	probabilistic	ADJ
ijassa-497	123	22	manner	manner	NOUN
ijassa-497	123	23	,	,	PUNCT
ijassa-497	123	24	therefore	therefore	ADV
ijassa-497	123	25	we	we	PRON
ijassa-497	123	26	propose	propose	VERB
ijassa-497	123	27	an	an	DET
ijassa-497	123	28	approach	approach	NOUN
ijassa-497	123	29	to	to	PART
ijassa-497	123	30	transform	transform	VERB
ijassa-497	123	31	the	the	DET
ijassa-497	123	32	anomaly	anomaly	NOUN
ijassa-497	123	33	score	score	NOUN
ijassa-497	123	34	produced	produce	VERB
ijassa-497	123	35	by	by	ADP
ijassa-497	123	36	expose	expose	VERB
ijassa-497	123	37	.	.	PUNCT
ijassa-497	124	1	to	to	ADP
ijassa-497	124	2	that	that	DET
ijassa-497	124	3	end	end	NOUN
ijassa-497	124	4	,	,	PUNCT
ijassa-497	124	5	the	the	DET
ijassa-497	124	6	idea	idea	NOUN
ijassa-497	124	7	of	of	ADP
ijassa-497	124	8	conformal	conformal	ADJ
ijassa-497	124	9	anomaly	anomaly	NOUN
ijassa-497	124	10	detection	detection	NOUN
ijassa-497	124	11	is	be	AUX
ijassa-497	124	12	adopted	adopt	VERB
ijassa-497	124	13	to	to	PART
ijassa-497	124	14	build	build	VERB
ijassa-497	124	15	an	an	DET
ijassa-497	124	16	anomaly	anomaly	NOUN
ijassa-497	124	17	detector	detector	NOUN
ijassa-497	124	18	for	for	ADP
ijassa-497	124	19	online	online	ADJ
ijassa-497	124	20	data	datum	NOUN
ijassa-497	124	21	.	.	PUNCT
ijassa-497	125	1	3	3	X
ijassa-497	125	2	.	.	X
ijassa-497	125	3	conformal	conformal	ADJ
ijassa-497	125	4	anomaly	anomaly	NOUN
ijassa-497	125	5	detection	detection	NOUN
ijassa-497	125	6	laxhammar	laxhammar	NOUN
ijassa-497	125	7	[	[	X
ijassa-497	125	8	16	16	NUM
ijassa-497	125	9	]	]	PUNCT
ijassa-497	125	10	proposed	propose	VERB
ijassa-497	125	11	a	a	DET
ijassa-497	125	12	conformal	conformal	ADJ
ijassa-497	125	13	anomaly	anomaly	NOUN
ijassa-497	125	14	detection	detection	NOUN
ijassa-497	125	15	(	(	PUNCT
ijassa-497	125	16	cad	cad	NOUN
ijassa-497	125	17	)	)	PUNCT
ijassa-497	125	18	which	which	PRON
ijassa-497	125	19	is	be	AUX
ijassa-497	125	20	a	a	DET
ijassa-497	125	21	distributionfree	distributionfree	NOUN
ijassa-497	125	22	procedure	procedure	NOUN
ijassa-497	125	23	for	for	ADP
ijassa-497	125	24	probability	probability	NOUN
ijassa-497	125	25	-	-	PUNCT
ijassa-497	125	26	like	like	ADJ
ijassa-497	125	27	confidence	confidence	NOUN
ijassa-497	125	28	measure	measure	NOUN
ijassa-497	125	29	estimation	estimation	NOUN
ijassa-497	125	30	based	base	VERB
ijassa-497	125	31	on	on	ADP
ijassa-497	125	32	non	non	ADJ
ijassa-497	125	33	-	-	ADJ
ijassa-497	125	34	conformity	conformity	ADJ
ijassa-497	125	35	measure	measure	NOUN
ijassa-497	125	36	(	(	PUNCT
ijassa-497	125	37	ncm	ncm	PROPN
ijassa-497	125	38	)	)	PUNCT
ijassa-497	125	39	provided	provide	VERB
ijassa-497	125	40	by	by	ADP
ijassa-497	125	41	some	some	DET
ijassa-497	125	42	detector	detector	NOUN
ijassa-497	125	43	.	.	PUNCT
ijassa-497	126	1	the	the	DET
ijassa-497	126	2	ncm	ncm	PROPN
ijassa-497	126	3	a(x	a(x	PROPN
ijassa-497	126	4	,	,	PUNCT
ijassa-497	126	5	y	y	NOUN
ijassa-497	126	6	)	)	PUNCT
ijassa-497	126	7	reflects	reflect	VERB
ijassa-497	126	8	how	how	SCONJ
ijassa-497	126	9	different	different	ADJ
ijassa-497	126	10	the	the	DET
ijassa-497	126	11	investigated	investigated	ADJ
ijassa-497	126	12	object	object	NOUN
ijassa-497	126	13	y	y	PROPN
ijassa-497	126	14	is	be	AUX
ijassa-497	126	15	from	from	ADP
ijassa-497	126	16	other	other	ADJ
ijassa-497	126	17	observations	observation	NOUN
ijassa-497	126	18	x.	x.	NOUN
ijassa-497	126	19	ncm	ncm	PROPN
ijassa-497	126	20	could	could	AUX
ijassa-497	126	21	be	be	AUX
ijassa-497	126	22	for	for	ADP
ijassa-497	126	23	instance	instance	NOUN
ijassa-497	126	24	the	the	DET
ijassa-497	126	25	average	average	ADJ
ijassa-497	126	26	distance	distance	NOUN
ijassa-497	126	27	to	to	ADP
ijassa-497	126	28	k	k	PROPN
ijassa-497	126	29	neighbours	neighbour	NOUN
ijassa-497	126	30	,	,	PUNCT
ijassa-497	126	31	the	the	DET
ijassa-497	126	32	distance	distance	NOUN
ijassa-497	126	33	to	to	ADP
ijassa-497	126	34	the	the	DET
ijassa-497	126	35	k	k	PROPN
ijassa-497	126	36	-	-	PUNCT
ijassa-497	126	37	th	th	X
ijassa-497	126	38	neighbour	neighbour	NOUN
ijassa-497	126	39	,	,	PUNCT
ijassa-497	126	40	residual	residual	ADJ
ijassa-497	126	41	in	in	ADP
ijassa-497	126	42	a	a	DET
ijassa-497	126	43	regression	regression	NOUN
ijassa-497	126	44	model	model	NOUN
ijassa-497	126	45	,	,	PUNCT
ijassa-497	126	46	to	to	PART
ijassa-497	126	47	name	name	VERB
ijassa-497	126	48	just	just	ADV
ijassa-497	126	49	a	a	DET
ijassa-497	126	50	few	few	ADJ
ijassa-497	126	51	.	.	PUNCT
ijassa-497	127	1	let	let	VERB
ijassa-497	127	2	us	we	PRON
ijassa-497	127	3	consider	consider	VERB
ijassa-497	127	4	a	a	DET
ijassa-497	127	5	time	time	NOUN
ijassa-497	127	6	series	series	NOUN
ijassa-497	127	7	xt	xt	PROPN
ijassa-497	127	8	,	,	PUNCT
ijassa-497	127	9	then	then	ADV
ijassa-497	127	10	compute	compute	VERB
ijassa-497	127	11	scores	score	NOUN
ijassa-497	127	12	ats	ats	PROPN
ijassa-497	127	13	=	=	SYM
ijassa-497	128	1	a(x−s	a(x−s	ADV
ijassa-497	128	2	:	:	PUNCT
ijassa-497	128	3	t	t	PROPN
ijassa-497	128	4	,	,	PUNCT
ijassa-497	128	5	xs	xs	PROPN
ijassa-497	128	6	)	)	PUNCT
ijassa-497	128	7	,	,	PUNCT
ijassa-497	128	8	s	s	NOUN
ijassa-497	128	9	=	=	NOUN
ijassa-497	128	10	1	1	NUM
ijassa-497	128	11	,	,	PUNCT
ijassa-497	128	12	.	.	PUNCT
ijassa-497	128	13	.	.	PUNCT
ijassa-497	129	1	.	.	PUNCT
ijassa-497	130	1	,	,	PUNCT
ijassa-497	130	2	t	t	PROPN
ijassa-497	130	3	,	,	PUNCT
ijassa-497	130	4	where	where	SCONJ
ijassa-497	130	5	a(x	a(x	NOUN
ijassa-497	130	6	,	,	PUNCT
ijassa-497	130	7	y	y	NOUN
ijassa-497	130	8	)	)	PUNCT
ijassa-497	130	9	is	be	AUX
ijassa-497	130	10	an	an	DET
ijassa-497	130	11	ncm	ncm	PROPN
ijassa-497	130	12	used	use	VERB
ijassa-497	130	13	by	by	ADP
ijassa-497	130	14	the	the	DET
ijassa-497	130	15	algorithm	algorithm	NOUN
ijassa-497	130	16	.	.	PUNCT
ijassa-497	131	1	then	then	ADV
ijassa-497	131	2	the	the	DET
ijassa-497	131	3	empirical	empirical	ADJ
ijassa-497	131	4	p	p	NOUN
ijassa-497	131	5	-	-	PUNCT
ijassa-497	131	6	value	value	NOUN
ijassa-497	131	7	is	be	AUX
ijassa-497	131	8	defined	define	VERB
ijassa-497	131	9	as	as	ADP
ijassa-497	131	10	:	:	PUNCT
ijassa-497	131	11	p(xt	p(xt	NOUN
ijassa-497	131	12	,	,	PUNCT
ijassa-497	131	13	x:(t−1	x:(t−1	PROPN
ijassa-497	131	14	)	)	PUNCT
ijassa-497	131	15	,	,	PUNCT
ijassa-497	131	16	a	a	X
ijassa-497	131	17	)	)	PUNCT
ijassa-497	131	18	=	=	SYM
ijassa-497	131	19	1	1	NUM
ijassa-497	131	20	t	t	NOUN
ijassa-497	131	21	|{s	|{s	NOUN
ijassa-497	131	22	=	=	SYM
ijassa-497	131	23	1	1	NUM
ijassa-497	131	24	,	,	PUNCT
ijassa-497	131	25	.	.	PUNCT
ijassa-497	131	26	.	.	PUNCT
ijassa-497	132	1	.	.	PUNCT
ijassa-497	133	1	,	,	PUNCT
ijassa-497	133	2	t	t	PROPN
ijassa-497	133	3	:	:	PUNCT
ijassa-497	133	4	ats	ats	PROPN
ijassa-497	133	5	≥	≥	PROPN
ijassa-497	133	6	att}|	att}|	X
ijassa-497	133	7	.	.	PUNCT
ijassa-497	134	1	the	the	PRON
ijassa-497	134	2	lower	low	ADJ
ijassa-497	134	3	it	it	PRON
ijassa-497	134	4	is	be	AUX
ijassa-497	134	5	,	,	PUNCT
ijassa-497	134	6	the	the	PRON
ijassa-497	134	7	lower	low	ADJ
ijassa-497	134	8	the	the	DET
ijassa-497	134	9	probability	probability	NOUN
ijassa-497	134	10	of	of	ADP
ijassa-497	134	11	falsely	falsely	ADV
ijassa-497	134	12	rejecting	reject	VERB
ijassa-497	134	13	the	the	DET
ijassa-497	134	14	null	null	ADJ
ijassa-497	134	15	hypothesis	hypothesis	NOUN
ijassa-497	134	16	(	(	PUNCT
ijassa-497	134	17	xt	xt	X
ijassa-497	134	18	is	be	AUX
ijassa-497	134	19	anomaly	anomaly	NOUN
ijassa-497	134	20	)	)	PUNCT
ijassa-497	134	21	is	be	AUX
ijassa-497	134	22	,	,	PUNCT
ijassa-497	134	23	thus	thus	ADV
ijassa-497	134	24	the	the	DET
ijassa-497	134	25	more	more	ADV
ijassa-497	134	26	likely	likely	ADJ
ijassa-497	134	27	xt	xt	PROPN
ijassa-497	134	28	is	be	AUX
ijassa-497	134	29	an	an	DET
ijassa-497	134	30	anomaly	anomaly	NOUN
ijassa-497	134	31	instance	instance	NOUN
ijassa-497	134	32	.	.	PUNCT
ijassa-497	135	1	shafer	shafer	PROPN
ijassa-497	135	2	and	and	CCONJ
ijassa-497	135	3	vovk	vovk	PROPN
ijassa-497	135	4	proved	prove	VERB
ijassa-497	135	5	[	[	X
ijassa-497	135	6	31	31	NUM
ijassa-497	135	7	]	]	PUNCT
ijassa-497	135	8	the	the	DET
ijassa-497	135	9	fact	fact	NOUN
ijassa-497	135	10	that	that	SCONJ
ijassa-497	135	11	cad	cad	PROPN
ijassa-497	135	12	could	could	AUX
ijassa-497	135	13	provide	provide	VERB
ijassa-497	135	14	the	the	DET
ijassa-497	135	15	following	follow	VERB
ijassa-497	135	16	guarantee	guarantee	NOUN
ijassa-497	135	17	when	when	SCONJ
ijassa-497	135	18	xt	xt	PROPN
ijassa-497	135	19	is	be	AUX
ijassa-497	135	20	i.i.d	i.i.d	ADJ
ijassa-497	135	21	:	:	PUNCT
ijassa-497	135	22	px∼d(p(xt	px∼d(p(xt	NOUN
ijassa-497	135	23	,	,	PUNCT
ijassa-497	135	24	x	x	PUNCT
ijassa-497	135	25	−t	−t	NOUN
ijassa-497	135	26	,	,	PUNCT
ijassa-497	135	27	a	a	PRON
ijassa-497	135	28	)	)	PUNCT
ijassa-497	135	29	<	<	X
ijassa-497	135	30	ε	ε	PROPN
ijassa-497	135	31	)	)	PUNCT
ijassa-497	135	32	≤	≤	NOUN
ijassa-497	135	33	ε	ε	PROPN
ijassa-497	135	34	,	,	PUNCT
ijassa-497	135	35	x	x	SYM
ijassa-497	135	36	=	=	SYM
ijassa-497	135	37	(	(	PUNCT
ijassa-497	135	38	xs	xs	PROPN
ijassa-497	135	39	)	)	PUNCT
ijassa-497	135	40	t	t	PROPN
ijassa-497	136	1	s=1	s=1	PROPN
ijassa-497	136	2	.	.	PUNCT
ijassa-497	137	1	it	it	PRON
ijassa-497	137	2	is	be	AUX
ijassa-497	137	3	clear	clear	ADJ
ijassa-497	137	4	that	that	SCONJ
ijassa-497	137	5	cad	cad	PROPN
ijassa-497	137	6	could	could	AUX
ijassa-497	137	7	be	be	AUX
ijassa-497	137	8	computationally	computationally	ADV
ijassa-497	137	9	heavy	heavy	ADJ
ijassa-497	137	10	as	as	SCONJ
ijassa-497	137	11	it	it	PRON
ijassa-497	137	12	requires	require	VERB
ijassa-497	137	13	computations	computation	NOUN
ijassa-497	137	14	of	of	ADP
ijassa-497	137	15	a(x−s	a(x−s	ADV
ijassa-497	137	16	:	:	PUNCT
ijassa-497	137	17	t	t	PROPN
ijassa-497	137	18	,	,	PUNCT
ijassa-497	137	19	xs	xs	PROPN
ijassa-497	137	20	)	)	PUNCT
ijassa-497	137	21	for	for	ADP
ijassa-497	137	22	s	s	PRON
ijassa-497	137	23	from	from	ADP
ijassa-497	137	24	1	1	NUM
ijassa-497	137	25	to	to	ADP
ijassa-497	137	26	t.	t.	NOUN
ijassa-497	137	27	to	to	PART
ijassa-497	137	28	mitigate	mitigate	VERB
ijassa-497	137	29	this	this	DET
ijassa-497	137	30	problem	problem	NOUN
ijassa-497	137	31	,	,	PUNCT
ijassa-497	137	32	an	an	DET
ijassa-497	137	33	inductive	inductive	ADJ
ijassa-497	137	34	conformal	conformal	NOUN
ijassa-497	137	35	anomaly	anomaly	NOUN
ijassa-497	137	36	detection	detection	NOUN
ijassa-497	137	37	(	(	PUNCT
ijassa-497	137	38	icad	icad	PROPN
ijassa-497	137	39	)	)	PUNCT
ijassa-497	137	40	was	be	AUX
ijassa-497	137	41	proposed	propose	VERB
ijassa-497	137	42	by	by	ADP
ijassa-497	137	43	laxhammar	laxhammar	NOUN
ijassa-497	137	44	and	and	CCONJ
ijassa-497	137	45	falkman	falkman	NOUN
ijassa-497	137	46	in	in	ADP
ijassa-497	137	47	[	[	X
ijassa-497	137	48	27	27	NUM
ijassa-497	137	49	]	]	PUNCT
ijassa-497	137	50	.	.	PUNCT
ijassa-497	138	1	this	this	DET
ijassa-497	138	2	approach	approach	NOUN
ijassa-497	138	3	relies	rely	VERB
ijassa-497	138	4	on	on	ADP
ijassa-497	138	5	scores	score	NOUN
ijassa-497	138	6	computed	compute	VERB
ijassa-497	138	7	on	on	ADP
ijassa-497	138	8	training	training	NOUN
ijassa-497	138	9	set	set	VERB
ijassa-497	138	10	x̄	x̄	NOUN
ijassa-497	138	11	for	for	ADP
ijassa-497	138	12	every	every	DET
ijassa-497	138	13	instance	instance	NOUN
ijassa-497	138	14	of	of	ADP
ijassa-497	138	15	the	the	DET
ijassa-497	138	16	calibration	calibration	NOUN
ijassa-497	138	17	set	set	VERB
ijassa-497	138	18	.	.	PUNCT
ijassa-497	139	1	for	for	ADP
ijassa-497	139	2	further	further	ADJ
ijassa-497	139	3	simplicity	simplicity	NOUN
ijassa-497	139	4	,	,	PUNCT
ijassa-497	139	5	let	let	VERB
ijassa-497	139	6	us	we	PRON
ijassa-497	139	7	consider	consider	VERB
ijassa-497	139	8	relabelled	relabelled	ADJ
ijassa-497	139	9	sequence	sequence	NOUN
ijassa-497	139	10	xt	xt	PROPN
ijassa-497	140	1	that	that	PRON
ijassa-497	140	2	starts	start	VERB
ijassa-497	140	3	from−n+	from−n+	NOUN
ijassa-497	141	1	1	1	X
ijassa-497	141	2	.	.	PUNCT
ijassa-497	141	3	then	then	ADV
ijassa-497	141	4	,	,	PUNCT
ijassa-497	141	5	icad	icad	PROPN
ijassa-497	141	6	has	have	VERB
ijassa-497	141	7	the	the	DET
ijassa-497	141	8	following	follow	VERB
ijassa-497	141	9	setup	setup	NOUN
ijassa-497	141	10	for	for	ADP
ijassa-497	141	11	every	every	DET
ijassa-497	141	12	t	t	PROPN
ijassa-497	141	13	≥	≥	NOUN
ijassa-497	141	14	1	1	NUM
ijassa-497	141	15	:	:	PUNCT
ijassa-497	141	16	x−n+1	x−n+1	PROPN
ijassa-497	141	17	,	,	PUNCT
ijassa-497	141	18	.	.	PUNCT
ijassa-497	141	19	.	.	PUNCT
ijassa-497	141	20	.	.	PUNCT
ijassa-497	142	1	,	,	PUNCT
ijassa-497	142	2	x0︸	x0︸	PROPN
ijassa-497	142	3	︷︷	︷︷	PROPN
ijassa-497	142	4	︸	︸	X
ijassa-497	142	5	x̄	x̄	PROPN
ijassa-497	142	6	training	training	NOUN
ijassa-497	142	7	,	,	PUNCT
ijassa-497	142	8	calibration︷	calibration︷	VERB
ijassa-497	142	9	︸︸	︸︸	PUNCT
ijassa-497	142	10	︷	︷	PUNCT
ijassa-497	143	1	x1,x2	x1,x2	NUM
ijassa-497	143	2	,	,	PUNCT
ijassa-497	143	3	.	.	PUNCT
ijassa-497	143	4	.	.	PUNCT
ijassa-497	143	5	.	.	PUNCT
ijassa-497	144	1	,	,	PUNCT
ijassa-497	144	2	xt−1,xt	xt−1,xt	PROPN
ijassa-497	144	3	.	.	PUNCT
ijassa-497	145	1	in	in	ADP
ijassa-497	145	2	that	that	DET
ijassa-497	145	3	setup	setup	NOUN
ijassa-497	145	4	,	,	PUNCT
ijassa-497	145	5	the	the	DET
ijassa-497	145	6	conformal	conformal	ADJ
ijassa-497	145	7	p	p	NOUN
ijassa-497	145	8	-	-	PUNCT
ijassa-497	145	9	value	value	NOUN
ijassa-497	145	10	of	of	ADP
ijassa-497	145	11	a	a	DET
ijassa-497	145	12	test	test	NOUN
ijassa-497	145	13	object	object	NOUN
ijassa-497	145	14	xt	xt	PROPN
ijassa-497	145	15	is	be	AUX
ijassa-497	145	16	computed	compute	VERB
ijassa-497	145	17	on	on	ADP
ijassa-497	145	18	the	the	DET
ijassa-497	145	19	basis	basis	NOUN
ijassa-497	145	20	of	of	ADP
ijassa-497	145	21	modified	modified	ADJ
ijassa-497	145	22	scores	score	NOUN
ijassa-497	145	23	:	:	PUNCT
ijassa-497	145	24	{	{	PUNCT
ijassa-497	145	25	ats	ats	PROPN
ijassa-497	145	26	=	=	PUNCT
ijassa-497	145	27	a(x̄,xs	a(x̄,xs	PROPN
ijassa-497	145	28	)	)	PUNCT
ijassa-497	145	29	,	,	PUNCT
ijassa-497	145	30	s	s	NOUN
ijassa-497	145	31	=	=	NOUN
ijassa-497	145	32	1	1	NUM
ijassa-497	145	33	,	,	PUNCT
ijassa-497	145	34	.	.	PUNCT
ijassa-497	145	35	.	.	PUNCT
ijassa-497	146	1	.	.	PUNCT
ijassa-497	147	1	,	,	PUNCT
ijassa-497	147	2	t	t	PROPN
ijassa-497	147	3	}	}	PUNCT
ijassa-497	147	4	,	,	PUNCT
ijassa-497	147	5	x̄	x̄	PUNCT
ijassa-497	147	6	=	=	PRON
ijassa-497	147	7	(	(	PUNCT
ijassa-497	147	8	x−n+1	x−n+1	PROPN
ijassa-497	147	9	,	,	PUNCT
ijassa-497	147	10	.	.	PUNCT
ijassa-497	147	11	.	.	PUNCT
ijassa-497	147	12	.	.	PUNCT
ijassa-497	148	1	,	,	PUNCT
ijassa-497	148	2	x0	x0	PROPN
ijassa-497	148	3	)	)	PUNCT
ijassa-497	148	4	.	.	PUNCT
ijassa-497	149	1	however	however	ADV
ijassa-497	149	2	,	,	PUNCT
ijassa-497	149	3	by	by	ADP
ijassa-497	149	4	relaxing	relax	VERB
ijassa-497	149	5	deterministic	deterministic	ADJ
ijassa-497	149	6	guarantee	guarantee	NOUN
ijassa-497	149	7	to	to	PART
ijassa-497	149	8	probably	probably	ADV
ijassa-497	149	9	approximately	approximately	ADV
ijassa-497	149	10	correct	correct	ADJ
ijassa-497	149	11	guarantee	guarantee	NOUN
ijassa-497	149	12	,	,	PUNCT
ijassa-497	149	13	it	it	PRON
ijassa-497	149	14	is	be	AUX
ijassa-497	149	15	achievable	achievable	ADJ
ijassa-497	149	16	to	to	PART
ijassa-497	149	17	adapt	adapt	VERB
ijassa-497	149	18	icad	icad	NOUN
ijassa-497	149	19	to	to	PART
ijassa-497	149	20	use	use	VERB
ijassa-497	149	21	only	only	ADV
ijassa-497	149	22	fixed	fix	VERB
ijassa-497	149	23	size	size	NOUN
ijassa-497	149	24	calibration	calibration	NOUN
ijassa-497	149	25	set	set	VERB
ijassa-497	149	26	.	.	PUNCT
ijassa-497	150	1	offline	offline	ADJ
ijassa-497	150	2	icad	icad	PROPN
ijassa-497	150	3	copyright	copyright	NOUN
ijassa-497	150	4	©	©	PROPN
ijassa-497	150	5	2017	2017	NUM
ijassa-497	150	6	assa	assa	NOUN
ijassa-497	150	7	.	.	PUNCT
ijassa-497	151	1	adv	adv	PROPN
ijassa-497	151	2	syst	syst	PROPN
ijassa-497	151	3	sci	sci	PROPN
ijassa-497	151	4	appl	appl	PROPN
ijassa-497	151	5	(	(	PUNCT
ijassa-497	151	6	2017	2017	NUM
ijassa-497	151	7	)	)	PUNCT
ijassa-497	151	8	conformal	conformal	ADJ
ijassa-497	151	9	kernel	kernel	NOUN
ijassa-497	151	10	expected	expect	VERB
ijassa-497	151	11	similarity	similarity	NOUN
ijassa-497	151	12	for	for	ADP
ijassa-497	151	13	anomaly	anomaly	NOUN
ijassa-497	151	14	detection	detection	NOUN
ijassa-497	151	15	in	in	ADP
ijassa-497	151	16	time	time	NOUN
ijassa-497	151	17	-	-	PUNCT
ijassa-497	151	18	series	series	NOUN
ijassa-497	151	19	data	datum	NOUN
ijassa-497	151	20	27	27	NUM
ijassa-497	151	21	was	be	AUX
ijassa-497	151	22	developed	develop	VERB
ijassa-497	151	23	to	to	PART
ijassa-497	151	24	use	use	VERB
ijassa-497	151	25	a	a	DET
ijassa-497	151	26	calibration	calibration	NOUN
ijassa-497	151	27	set	set	VERB
ijassa-497	151	28	only	only	ADV
ijassa-497	151	29	with	with	ADP
ijassa-497	151	30	fixed	fix	VERB
ijassa-497	151	31	size	size	NOUN
ijassa-497	151	32	m	m	PROPN
ijassa-497	151	33	,	,	PUNCT
ijassa-497	151	34	sliding	slide	VERB
ijassa-497	151	35	along	along	ADP
ijassa-497	151	36	the	the	DET
ijassa-497	151	37	time	time	NOUN
ijassa-497	151	38	series	series	NOUN
ijassa-497	151	39	,	,	PUNCT
ijassa-497	151	40	as	as	SCONJ
ijassa-497	151	41	illustrated	illustrate	VERB
ijassa-497	151	42	:	:	PUNCT
ijassa-497	151	43	x−n+1	x−n+1	PROPN
ijassa-497	151	44	,	,	PUNCT
ijassa-497	151	45	.	.	PUNCT
ijassa-497	151	46	.	.	PUNCT
ijassa-497	151	47	.	.	PUNCT
ijassa-497	152	1	,	,	PUNCT
ijassa-497	152	2	x0︸	x0︸	PROPN
ijassa-497	152	3	︷︷	︷︷	PROPN
ijassa-497	152	4	︸	︸	X
ijassa-497	152	5	x̄	x̄	NOUN
ijassa-497	152	6	training	training	NOUN
ijassa-497	152	7	,	,	PUNCT
ijassa-497	152	8	.	.	PUNCT
ijassa-497	152	9	.	.	PUNCT
ijassa-497	153	1	.	.	PUNCT
ijassa-497	154	1	,	,	PUNCT
ijassa-497	154	2	calibration︷	calibration︷	VERB
ijassa-497	154	3	︸︸	︸︸	PUNCT
ijassa-497	154	4	︷	︷	PROPN
ijassa-497	154	5	xt−m	xt−m	PROPN
ijassa-497	154	6	,	,	PUNCT
ijassa-497	154	7	xt−m+1	xt−m+1	PROPN
ijassa-497	154	8	,	,	PUNCT
ijassa-497	154	9	.	.	PUNCT
ijassa-497	154	10	.	.	PUNCT
ijassa-497	154	11	.	.	PUNCT
ijassa-497	155	1	,	,	PUNCT
ijassa-497	155	2	xt−1,xt	xt−1,xt	PROPN
ijassa-497	155	3	.	.	PUNCT
ijassa-497	156	1	it	it	PRON
ijassa-497	156	2	should	should	AUX
ijassa-497	156	3	be	be	AUX
ijassa-497	156	4	highlighted	highlight	VERB
ijassa-497	156	5	that	that	SCONJ
ijassa-497	156	6	the	the	DET
ijassa-497	156	7	conformal	conformal	ADJ
ijassa-497	156	8	p	p	NOUN
ijassa-497	156	9	-	-	PUNCT
ijassa-497	156	10	value	value	NOUN
ijassa-497	156	11	in	in	ADP
ijassa-497	156	12	this	this	DET
ijassa-497	156	13	case	case	NOUN
ijassa-497	156	14	uses	use	VERB
ijassa-497	156	15	a	a	DET
ijassa-497	156	16	subsample	subsample	NOUN
ijassa-497	156	17	of	of	ADP
ijassa-497	156	18	the	the	DET
ijassa-497	156	19	icad	icad	PROPN
ijassa-497	156	20	non	non	ADJ
ijassa-497	156	21	-	-	ADJ
ijassa-497	156	22	conformity	conformity	ADJ
ijassa-497	156	23	scores	score	NOUN
ijassa-497	156	24	:	:	PUNCT
ijassa-497	156	25	p(xt	p(xt	NOUN
ijassa-497	156	26	,	,	PUNCT
ijassa-497	156	27	x:(t−1	x:(t−1	PROPN
ijassa-497	156	28	)	)	PUNCT
ijassa-497	156	29	,	,	PUNCT
ijassa-497	156	30	a	a	X
ijassa-497	156	31	)	)	PUNCT
ijassa-497	156	32	=	=	SYM
ijassa-497	157	1	1	1	NUM
ijassa-497	157	2	m+	m+	NUM
ijassa-497	157	3	1	1	NUM
ijassa-497	157	4	|{s	|{s	NOUN
ijassa-497	157	5	=	=	SYM
ijassa-497	157	6	0	0	NUM
ijassa-497	157	7	,	,	PUNCT
ijassa-497	157	8	.	.	PUNCT
ijassa-497	157	9	.	.	PUNCT
ijassa-497	157	10	.	.	PUNCT
ijassa-497	158	1	,	,	PUNCT
ijassa-497	158	2	m	m	PROPN
ijassa-497	158	3	:	:	PUNCT
ijassa-497	158	4	att−s	att−s	PRON
ijassa-497	158	5	≥	≥	NOUN
ijassa-497	158	6	att}|	att}|	X
ijassa-497	158	7	.	.	PUNCT
ijassa-497	159	1	vovk	vovk	PROPN
ijassa-497	159	2	proved	prove	VERB
ijassa-497	159	3	[	[	X
ijassa-497	159	4	28	28	NUM
ijassa-497	159	5	]	]	X
ijassa-497	159	6	the	the	DET
ijassa-497	159	7	following	follow	VERB
ijassa-497	159	8	guarantee	guarantee	NOUN
ijassa-497	159	9	for	for	ADP
ijassa-497	159	10	the	the	DET
ijassa-497	159	11	offline	offline	ADJ
ijassa-497	159	12	icad	icad	NOUN
ijassa-497	159	13	:	:	PUNCT
ijassa-497	159	14	px∼d(p(x	px∼d(p(x	NOUN
ijassa-497	159	15	,	,	PUNCT
ijassa-497	159	16	x	x	NOUN
ijassa-497	159	17	,	,	PUNCT
ijassa-497	159	18	ā	ā	ADJ
ijassa-497	159	19	)	)	PUNCT
ijassa-497	159	20	<	<	X
ijassa-497	159	21	ε	ε	PROPN
ijassa-497	159	22	)	)	PUNCT
ijassa-497	159	23	≤	≤	NOUN
ijassa-497	159	24	ε+	ε+	X
ijassa-497	159	25	√	√	NUM
ijassa-497	159	26	log	log	VERB
ijassa-497	159	27	1	1	NUM
ijassa-497	159	28	δ	δ	NOUN
ijassa-497	159	29	2	2	NUM
ijassa-497	159	30	m	m	NOUN
ijassa-497	159	31	.	.	PUNCT
ijassa-497	160	1	4	4	X
ijassa-497	160	2	.	.	NUM
ijassa-497	160	3	proposed	propose	VERB
ijassa-497	160	4	approach	approach	NOUN
ijassa-497	160	5	for	for	ADP
ijassa-497	160	6	anomaly	anomaly	NOUN
ijassa-497	160	7	detection	detection	NOUN
ijassa-497	160	8	in	in	ADP
ijassa-497	160	9	time	time	NOUN
ijassa-497	160	10	series	series	PROPN
ijassa-497	160	11	data	datum	NOUN
ijassa-497	160	12	in	in	ADP
ijassa-497	160	13	this	this	DET
ijassa-497	160	14	section	section	NOUN
ijassa-497	160	15	we	we	PRON
ijassa-497	160	16	outline	outline	VERB
ijassa-497	160	17	the	the	DET
ijassa-497	160	18	proposed	propose	VERB
ijassa-497	160	19	algorithm	algorithm	NOUN
ijassa-497	160	20	for	for	ADP
ijassa-497	160	21	anomaly	anomaly	NOUN
ijassa-497	160	22	detection	detection	NOUN
ijassa-497	160	23	in	in	ADP
ijassa-497	160	24	time	time	NOUN
ijassa-497	160	25	series	series	NOUN
ijassa-497	160	26	.	.	PUNCT
ijassa-497	161	1	it	it	PRON
ijassa-497	161	2	is	be	AUX
ijassa-497	161	3	worth	worth	ADJ
ijassa-497	161	4	emphasising	emphasise	VERB
ijassa-497	161	5	that	that	SCONJ
ijassa-497	161	6	the	the	DET
ijassa-497	161	7	developed	develop	VERB
ijassa-497	161	8	approach	approach	NOUN
ijassa-497	161	9	does	do	AUX
ijassa-497	161	10	not	not	PART
ijassa-497	161	11	require	require	VERB
ijassa-497	161	12	any	any	DET
ijassa-497	161	13	assumption	assumption	NOUN
ijassa-497	161	14	about	about	ADP
ijassa-497	161	15	the	the	DET
ijassa-497	161	16	data	datum	NOUN
ijassa-497	161	17	distribution	distribution	NOUN
ijassa-497	161	18	and	and	CCONJ
ijassa-497	161	19	it	it	PRON
ijassa-497	161	20	outputs	output	VERB
ijassa-497	161	21	a	a	DET
ijassa-497	161	22	probabilistic	probabilistic	ADJ
ijassa-497	161	23	measure	measure	NOUN
ijassa-497	161	24	of	of	ADP
ijassa-497	161	25	anomality	anomality	NOUN
ijassa-497	161	26	based	base	VERB
ijassa-497	161	27	on	on	ADP
ijassa-497	161	28	nonconformity	nonconformity	NOUN
ijassa-497	161	29	scores	score	NOUN
ijassa-497	161	30	.	.	PUNCT
ijassa-497	162	1	such	such	ADJ
ijassa-497	162	2	measure	measure	NOUN
ijassa-497	162	3	of	of	ADP
ijassa-497	162	4	anomality	anomality	NOUN
ijassa-497	162	5	is	be	AUX
ijassa-497	162	6	calculated	calculate	VERB
ijassa-497	162	7	using	use	VERB
ijassa-497	162	8	an	an	DET
ijassa-497	162	9	adaptation	adaptation	NOUN
ijassa-497	162	10	of	of	ADP
ijassa-497	162	11	icad	icad	NOUN
ijassa-497	162	12	to	to	ADP
ijassa-497	162	13	the	the	DET
ijassa-497	162	14	case	case	NOUN
ijassa-497	162	15	of	of	ADP
ijassa-497	162	16	potentially	potentially	ADV
ijassa-497	162	17	non	non	ADJ
ijassa-497	162	18	-	-	ADJ
ijassa-497	162	19	stationary	stationary	ADJ
ijassa-497	162	20	and	and	CCONJ
ijassa-497	162	21	quasi	quasi	ADJ
ijassa-497	162	22	-	-	ADJ
ijassa-497	162	23	periodic	periodic	ADJ
ijassa-497	162	24	time	time	NOUN
ijassa-497	162	25	series	series	NOUN
ijassa-497	162	26	.	.	PUNCT
ijassa-497	163	1	cad	cad	PROPN
ijassa-497	163	2	and	and	CCONJ
ijassa-497	163	3	online	online	PROPN
ijassa-497	163	4	icad	icad	PROPN
ijassa-497	163	5	are	be	AUX
ijassa-497	163	6	computationally	computationally	ADV
ijassa-497	163	7	complex	complex	ADJ
ijassa-497	163	8	,	,	PUNCT
ijassa-497	163	9	therefore	therefore	ADV
ijassa-497	163	10	offline	offline	ADJ
ijassa-497	163	11	icad	icad	PROPN
ijassa-497	163	12	seems	seem	VERB
ijassa-497	163	13	much	much	ADV
ijassa-497	163	14	suitable	suitable	ADJ
ijassa-497	163	15	for	for	ADP
ijassa-497	163	16	the	the	DET
ijassa-497	163	17	task	task	NOUN
ijassa-497	163	18	.	.	PUNCT
ijassa-497	164	1	nevertheless	nevertheless	ADV
ijassa-497	164	2	,	,	PUNCT
ijassa-497	164	3	as	as	SCONJ
ijassa-497	164	4	offline	offline	ADJ
ijassa-497	164	5	icad	icad	PROPN
ijassa-497	164	6	uses	use	VERB
ijassa-497	164	7	a	a	DET
ijassa-497	164	8	fixed	fix	VERB
ijassa-497	164	9	training	training	NOUN
ijassa-497	164	10	set	set	NOUN
ijassa-497	164	11	,	,	PUNCT
ijassa-497	164	12	it	it	PRON
ijassa-497	164	13	should	should	AUX
ijassa-497	164	14	be	be	AUX
ijassa-497	164	15	noticed	notice	VERB
ijassa-497	164	16	that	that	SCONJ
ijassa-497	164	17	one	one	PRON
ijassa-497	164	18	could	could	AUX
ijassa-497	164	19	face	face	VERB
ijassa-497	164	20	problems	problem	NOUN
ijassa-497	164	21	in	in	ADP
ijassa-497	164	22	case	case	NOUN
ijassa-497	164	23	of	of	ADP
ijassa-497	164	24	non	non	ADJ
ijassa-497	164	25	-	-	ADJ
ijassa-497	164	26	stationary	stationary	ADJ
ijassa-497	164	27	time	time	NOUN
ijassa-497	164	28	series	series	NOUN
ijassa-497	164	29	.	.	PUNCT
ijassa-497	165	1	in	in	ADP
ijassa-497	165	2	the	the	DET
ijassa-497	165	3	light	light	NOUN
ijassa-497	165	4	of	of	ADP
ijassa-497	165	5	the	the	DET
ijassa-497	165	6	discussed	discuss	VERB
ijassa-497	165	7	details	detail	NOUN
ijassa-497	165	8	and	and	CCONJ
ijassa-497	165	9	difficulties	difficulty	NOUN
ijassa-497	165	10	,	,	PUNCT
ijassa-497	165	11	lazy	lazy	ADJ
ijassa-497	165	12	drifting	drift	VERB
ijassa-497	165	13	conformal	conformal	ADJ
ijassa-497	165	14	detector	detector	NOUN
ijassa-497	165	15	(	(	PUNCT
ijassa-497	165	16	ldcd	ldcd	PROPN
ijassa-497	165	17	)	)	PUNCT
ijassa-497	165	18	has	have	AUX
ijassa-497	165	19	been	be	AUX
ijassa-497	165	20	proposed	propose	VERB
ijassa-497	165	21	in	in	ADP
ijassa-497	165	22	our	our	PRON
ijassa-497	165	23	paper	paper	NOUN
ijassa-497	165	24	[	[	X
ijassa-497	165	25	32	32	NUM
ijassa-497	165	26	]	]	PUNCT
ijassa-497	165	27	.	.	PUNCT
ijassa-497	166	1	for	for	ADP
ijassa-497	166	2	simplicity	simplicity	NOUN
ijassa-497	166	3	we	we	PRON
ijassa-497	166	4	consider	consider	VERB
ijassa-497	166	5	a	a	DET
ijassa-497	166	6	univariate	univariate	ADJ
ijassa-497	166	7	time	time	NOUN
ijassa-497	166	8	-	-	PUNCT
ijassa-497	166	9	series	series	NOUN
ijassa-497	166	10	x	x	X
ijassa-497	166	11	=	=	SYM
ijassa-497	166	12	(	(	PUNCT
ijassa-497	166	13	xt)t≥1	xt)t≥1	PROPN
ijassa-497	166	14	∈	∈	PROPN
ijassa-497	166	15	r	r	NOUN
ijassa-497	166	16	,	,	PUNCT
ijassa-497	166	17	although	although	SCONJ
ijassa-497	166	18	our	our	PRON
ijassa-497	166	19	approach	approach	NOUN
ijassa-497	166	20	is	be	AUX
ijassa-497	166	21	valid	valid	ADJ
ijassa-497	166	22	for	for	ADP
ijassa-497	166	23	multivariate	multivariate	NOUN
ijassa-497	166	24	data	datum	NOUN
ijassa-497	166	25	as	as	ADV
ijassa-497	166	26	well	well	ADV
ijassa-497	166	27	since	since	SCONJ
ijassa-497	166	28	we	we	PRON
ijassa-497	166	29	are	be	AUX
ijassa-497	166	30	going	go	VERB
ijassa-497	166	31	to	to	PART
ijassa-497	166	32	use	use	VERB
ijassa-497	166	33	kernel	kernel	NOUN
ijassa-497	166	34	-	-	PUNCT
ijassa-497	166	35	based	base	VERB
ijassa-497	166	36	non	non	ADJ
ijassa-497	166	37	-	-	ADJ
ijassa-497	166	38	conformity	conformity	ADJ
ijassa-497	166	39	measure	measure	NOUN
ijassa-497	166	40	.	.	PUNCT
ijassa-497	167	1	to	to	PART
ijassa-497	167	2	begin	begin	VERB
ijassa-497	167	3	with	with	ADP
ijassa-497	167	4	,	,	PUNCT
ijassa-497	167	5	the	the	DET
ijassa-497	167	6	time	time	NOUN
ijassa-497	167	7	series	series	NOUN
ijassa-497	167	8	x	x	PRON
ijassa-497	167	9	should	should	AUX
ijassa-497	167	10	be	be	AUX
ijassa-497	167	11	embedded	embed	VERB
ijassa-497	167	12	into	into	ADP
ijassa-497	167	13	l	l	ADJ
ijassa-497	167	14	-	-	ADJ
ijassa-497	167	15	dimensional	dimensional	ADJ
ijassa-497	167	16	space	space	NOUN
ijassa-497	167	17	.	.	PUNCT
ijassa-497	168	1	to	to	ADP
ijassa-497	168	2	that	that	DET
ijassa-497	168	3	end	end	NOUN
ijassa-497	168	4	,	,	PUNCT
ijassa-497	168	5	we	we	PRON
ijassa-497	168	6	further	far	ADV
ijassa-497	168	7	consider	consider	VERB
ijassa-497	168	8	the	the	DET
ijassa-497	168	9	sequence	sequence	NOUN
ijassa-497	168	10	of	of	ADP
ijassa-497	168	11	xt	xt	PROPN
ijassa-497	168	12	=	=	SYM
ijassa-497	168	13	(	(	PUNCT
ijassa-497	168	14	xt−l+1	xt−l+1	PROPN
ijassa-497	168	15	,	,	PUNCT
ijassa-497	168	16	.	.	PUNCT
ijassa-497	168	17	.	.	PUNCT
ijassa-497	169	1	.	.	PUNCT
ijassa-497	170	1	,	,	PUNCT
ijassa-497	170	2	xt	xt	X
ijassa-497	170	3	)	)	PUNCT
ijassa-497	170	4	∈	∈	PROPN
ijassa-497	170	5	rl	rl	PROPN
ijassa-497	170	6	constructed	construct	VERB
ijassa-497	170	7	by	by	ADP
ijassa-497	170	8	moving	move	VERB
ijassa-497	170	9	window	window	NOUN
ijassa-497	170	10	of	of	ADP
ijassa-497	170	11	the	the	DET
ijassa-497	170	12	width	width	ADJ
ijassa-497	170	13	l	l	NOUN
ijassa-497	170	14	on	on	ADP
ijassa-497	170	15	the	the	DET
ijassa-497	170	16	time	time	NOUN
ijassa-497	170	17	series	series	PROPN
ijassa-497	170	18	x	x	NOUN
ijassa-497	170	19	:	:	PUNCT
ijassa-497	170	20	.	.	PUNCT
ijassa-497	170	21	.	.	PUNCT
ijassa-497	171	1	.	.	PUNCT
ijassa-497	172	1	,	,	PUNCT
ijassa-497	172	2	xt−l−1	xt−l−1	PROPN
ijassa-497	172	3	,	,	PUNCT
ijassa-497	172	4	xt−1	xt−1	PROPN
ijassa-497	172	5	xt−l	xt−l	PROPN
ijassa-497	172	6	,	,	PUNCT
ijassa-497	172	7	xt−l+1	xt−l+1	PROPN
ijassa-497	172	8	,	,	PUNCT
ijassa-497	172	9	.	.	PUNCT
ijassa-497	172	10	.	.	PUNCT
ijassa-497	172	11	.	.	PUNCT
ijassa-497	173	1	,	,	PUNCT
ijassa-497	173	2	xt−1	xt−1	PROPN
ijassa-497	173	3	,	,	PUNCT
ijassa-497	173	4	xt	xt	PROPN
ijassa-497	173	5	xt	xt	X
ijassa-497	173	6	,	,	PUNCT
ijassa-497	173	7	xt+1	xt+1	PROPN
ijassa-497	173	8	,	,	PUNCT
ijassa-497	173	9	.	.	PUNCT
ijassa-497	173	10	.	.	PUNCT
ijassa-497	173	11	.	.	PUNCT
ijassa-497	173	12	.	.	PUNCT
ijassa-497	174	1	it	it	PRON
ijassa-497	174	2	should	should	AUX
ijassa-497	174	3	be	be	AUX
ijassa-497	174	4	noticed	notice	VERB
ijassa-497	174	5	that	that	SCONJ
ijassa-497	174	6	such	such	ADJ
ijassa-497	174	7	approach	approach	NOUN
ijassa-497	174	8	obviously	obviously	ADV
ijassa-497	174	9	produce	produce	VERB
ijassa-497	174	10	t−	t−	PRON
ijassa-497	174	11	l+	l+	ADJ
ijassa-497	174	12	1	1	NUM
ijassa-497	174	13	embeddings	embedding	NOUN
ijassa-497	174	14	from	from	ADP
ijassa-497	174	15	the	the	DET
ijassa-497	174	16	sequence	sequence	NOUN
ijassa-497	174	17	of	of	ADP
ijassa-497	174	18	the	the	DET
ijassa-497	174	19	length	length	NOUN
ijassa-497	174	20	t.	t.	PROPN
ijassa-497	174	21	in	in	ADP
ijassa-497	174	22	other	other	ADJ
ijassa-497	174	23	words	word	NOUN
ijassa-497	174	24	,	,	PUNCT
ijassa-497	174	25	to	to	PART
ijassa-497	174	26	produce	produce	VERB
ijassa-497	174	27	the	the	DET
ijassa-497	174	28	first	first	ADJ
ijassa-497	174	29	such	such	ADJ
ijassa-497	174	30	embedding	embed	VERB
ijassa-497	174	31	,	,	PUNCT
ijassa-497	174	32	we	we	PRON
ijassa-497	174	33	need	need	VERB
ijassa-497	174	34	to	to	PART
ijassa-497	174	35	observe	observe	VERB
ijassa-497	174	36	l	l	NOUN
ijassa-497	174	37	instances	instance	NOUN
ijassa-497	174	38	initially	initially	ADV
ijassa-497	174	39	.	.	PUNCT
ijassa-497	175	1	as	as	ADP
ijassa-497	175	2	ncm	ncm	PROPN
ijassa-497	175	3	we	we	PRON
ijassa-497	175	4	are	be	AUX
ijassa-497	175	5	using	use	VERB
ijassa-497	175	6	the	the	DET
ijassa-497	175	7	expected	expect	VERB
ijassa-497	175	8	similarity	similarity	NOUN
ijassa-497	175	9	:	:	PUNCT
ijassa-497	175	10	a(tt	a(tt	NUM
ijassa-497	175	11	,	,	PUNCT
ijassa-497	175	12	xt	xt	ADJ
ijassa-497	175	13	)	)	PUNCT
ijassa-497	175	14	=	=	SYM
ijassa-497	176	1	1	1	NUM
ijassa-497	176	2	n	n	NUM
ijassa-497	176	3	n∑	n∑	NOUN
ijassa-497	176	4	i=1	i=1	PROPN
ijassa-497	177	1	k(xt	k(xt	NOUN
ijassa-497	177	2	,	,	PUNCT
ijassa-497	177	3	xt−m−i	xt−m−i	PROPN
ijassa-497	177	4	)	)	PUNCT
ijassa-497	177	5	,	,	PUNCT
ijassa-497	177	6	where	where	SCONJ
ijassa-497	177	7	tt	tt	PROPN
ijassa-497	177	8	=	=	X
ijassa-497	177	9	{	{	PUNCT
ijassa-497	177	10	xs	xs	NOUN
ijassa-497	177	11	:	:	PUNCT
ijassa-497	177	12	s	s	X
ijassa-497	177	13	=	=	SYM
ijassa-497	177	14	t−m−	t−m−	PROPN
ijassa-497	177	15	n	n	CCONJ
ijassa-497	177	16	,	,	PUNCT
ijassa-497	177	17	.	.	PUNCT
ijassa-497	177	18	.	.	PUNCT
ijassa-497	177	19	.	.	PUNCT
ijassa-497	178	1	,	,	PUNCT
ijassa-497	178	2	t−m−	t−m−	NOUN
ijassa-497	178	3	1	1	NUM
ijassa-497	178	4	}	}	PUNCT
ijassa-497	178	5	,	,	PUNCT
ijassa-497	178	6	k(x	k(x	PROPN
ijassa-497	178	7	,	,	PUNCT
ijassa-497	178	8	y	y	NOUN
ijassa-497	178	9	)	)	PUNCT
ijassa-497	178	10	is	be	AUX
ijassa-497	178	11	a	a	DET
ijassa-497	178	12	kernel	kernel	NOUN
ijassa-497	178	13	function	function	NOUN
ijassa-497	178	14	.	.	PUNCT
ijassa-497	179	1	data	datum	NOUN
ijassa-497	179	2	:	:	PUNCT
ijassa-497	179	3	.	.	PUNCT
ijassa-497	179	4	.	.	PUNCT
ijassa-497	179	5	.	.	PUNCT
ijassa-497	180	1	,	,	PUNCT
ijassa-497	180	2	tt	tt	PROPN
ijassa-497	180	3	training︷	training︷	VERB
ijassa-497	180	4	︸︸	︸︸	PUNCT
ijassa-497	180	5	︷	︷	PROPN
ijassa-497	181	1	xt−m−n	xt−m−n	PROPN
ijassa-497	181	2	,	,	PUNCT
ijassa-497	181	3	.	.	PUNCT
ijassa-497	181	4	.	.	PUNCT
ijassa-497	181	5	.	.	PUNCT
ijassa-497	182	1	,	,	PUNCT
ijassa-497	182	2	xt−m−1	xt−m−1	PROPN
ijassa-497	182	3	,	,	PUNCT
ijassa-497	182	4	xt−m	xt−m	PROPN
ijassa-497	182	5	,	,	PUNCT
ijassa-497	182	6	.	.	PUNCT
ijassa-497	182	7	.	.	PUNCT
ijassa-497	182	8	.	.	PUNCT
ijassa-497	183	1	,	,	PUNCT
ijassa-497	183	2	xt−1	xt−1	PROPN
ijassa-497	183	3	,	,	PUNCT
ijassa-497	183	4	test	test	NOUN
ijassa-497	183	5	xt	xt	ADP
ijassa-497	183	6	,	,	PUNCT
ijassa-497	183	7	.	.	PUNCT
ijassa-497	183	8	.	.	PUNCT
ijassa-497	183	9	.	.	PUNCT
ijassa-497	184	1	scores	score	NOUN
ijassa-497	184	2	:	:	PUNCT
ijassa-497	184	3	.	.	PUNCT
ijassa-497	184	4	.	.	PUNCT
ijassa-497	184	5	.	.	PUNCT
ijassa-497	185	1	,	,	PUNCT
ijassa-497	185	2	at−m−n	at−m−n	NOUN
ijassa-497	185	3	,	,	PUNCT
ijassa-497	185	4	.	.	PUNCT
ijassa-497	185	5	.	.	PUNCT
ijassa-497	186	1	.	.	PUNCT
ijassa-497	187	1	,	,	PUNCT
ijassa-497	187	2	at−m−1	at−m−1	PROPN
ijassa-497	187	3	,	,	PUNCT
ijassa-497	187	4	at−m	at−m	PROPN
ijassa-497	187	5	,	,	PUNCT
ijassa-497	187	6	.	.	PUNCT
ijassa-497	187	7	.	.	PUNCT
ijassa-497	187	8	.	.	PUNCT
ijassa-497	188	1	,	,	PUNCT
ijassa-497	188	2	at−1︸	at−1︸	PROPN
ijassa-497	188	3	︷︷	︷︷	PROPN
ijassa-497	188	4	︸	︸	X
ijassa-497	188	5	at	at	ADP
ijassa-497	188	6	calibration	calibration	NOUN
ijassa-497	188	7	,	,	PUNCT
ijassa-497	188	8	at	at	ADP
ijassa-497	188	9	test	test	NOUN
ijassa-497	188	10	,	,	PUNCT
ijassa-497	188	11	.	.	PUNCT
ijassa-497	188	12	.	.	PUNCT
ijassa-497	188	13	.	.	PUNCT
ijassa-497	189	1	copyright	copyright	NOUN
ijassa-497	189	2	©	©	PROPN
ijassa-497	189	3	2017	2017	NUM
ijassa-497	189	4	assa	assa	NOUN
ijassa-497	189	5	.	.	PUNCT
ijassa-497	190	1	adv	adv	PROPN
ijassa-497	190	2	syst	syst	PROPN
ijassa-497	190	3	sci	sci	PROPN
ijassa-497	190	4	appl	appl	PROPN
ijassa-497	190	5	(	(	PUNCT
ijassa-497	190	6	2017	2017	NUM
ijassa-497	190	7	)	)	PUNCT
ijassa-497	190	8	28	28	NUM
ijassa-497	190	9	aleksandr	aleksandr	PROPN
ijassa-497	190	10	safin	safin	PROPN
ijassa-497	190	11	,	,	PUNCT
ijassa-497	190	12	evgeny	evgeny	PROPN
ijassa-497	190	13	burnaev	burnaev	PROPN
ijassa-497	190	14	the	the	DET
ijassa-497	190	15	proposed	propose	VERB
ijassa-497	190	16	approach	approach	NOUN
ijassa-497	190	17	could	could	AUX
ijassa-497	190	18	be	be	AUX
ijassa-497	190	19	described	describe	VERB
ijassa-497	190	20	as	as	SCONJ
ijassa-497	190	21	follows	follow	VERB
ijassa-497	190	22	:	:	PUNCT
ijassa-497	190	23	1	1	X
ijassa-497	190	24	.	.	X
ijassa-497	190	25	construct	construct	VERB
ijassa-497	190	26	time	time	NOUN
ijassa-497	190	27	series	series	PROPN
ijassa-497	190	28	embedding	embed	VERB
ijassa-497	190	29	in	in	ADP
ijassa-497	190	30	a	a	DET
ijassa-497	190	31	sliding	slide	VERB
ijassa-497	190	32	window	window	NOUN
ijassa-497	190	33	,	,	PUNCT
ijassa-497	190	34	2	2	X
ijassa-497	190	35	.	.	X
ijassa-497	190	36	compute	compute	VERB
ijassa-497	190	37	an+s	an+s	NOUN
ijassa-497	190	38	=	=	SYM
ijassa-497	190	39	a(tn+s	a(tn+s	PROPN
ijassa-497	190	40	,	,	PUNCT
ijassa-497	190	41	xn+s	xn+s	PROPN
ijassa-497	190	42	)	)	PUNCT
ijassa-497	190	43	,	,	PUNCT
ijassa-497	190	44	s	s	NOUN
ijassa-497	190	45	=	=	NOUN
ijassa-497	190	46	1	1	NUM
ijassa-497	190	47	,	,	PUNCT
ijassa-497	190	48	.	.	PUNCT
ijassa-497	190	49	.	.	PUNCT
ijassa-497	190	50	.	.	PUNCT
ijassa-497	191	1	,	,	PUNCT
ijassa-497	191	2	m+	m+	NOUN
ijassa-497	191	3	1	1	NUM
ijassa-497	191	4	,	,	PUNCT
ijassa-497	191	5	3	3	NUM
ijassa-497	191	6	.	.	X
ijassa-497	191	7	evaluate	evaluate	VERB
ijassa-497	191	8	the	the	DET
ijassa-497	191	9	empirical	empirical	ADJ
ijassa-497	191	10	p	p	NOUN
ijassa-497	191	11	-	-	PUNCT
ijassa-497	191	12	value	value	NOUN
ijassa-497	191	13	of	of	ADP
ijassa-497	191	14	its	its	PRON
ijassa-497	191	15	non	non	ADJ
ijassa-497	191	16	-	-	ADJ
ijassa-497	191	17	conformity	conformity	ADJ
ijassa-497	191	18	score	score	NOUN
ijassa-497	191	19	:	:	PUNCT
ijassa-497	191	20	p(xt	p(xt	NOUN
ijassa-497	191	21	,	,	PUNCT
ijassa-497	191	22	tt	tt	PROPN
ijassa-497	191	23	,	,	PUNCT
ijassa-497	191	24	a	a	PRON
ijassa-497	191	25	)	)	PUNCT
ijassa-497	191	26	=	=	SYM
ijassa-497	192	1	1	1	NUM
ijassa-497	192	2	m+	m+	NUM
ijassa-497	192	3	1	1	NUM
ijassa-497	192	4	|{i	|{i	X
ijassa-497	192	5	=	=	NOUN
ijassa-497	192	6	0	0	NUM
ijassa-497	192	7	,	,	PUNCT
ijassa-497	192	8	.	.	PUNCT
ijassa-497	192	9	.	.	PUNCT
ijassa-497	192	10	.	.	PUNCT
ijassa-497	193	1	,	,	PUNCT
ijassa-497	193	2	m	m	VERB
ijassa-497	193	3	:	:	PUNCT
ijassa-497	193	4	at−i	at−i	PROPN
ijassa-497	193	5	≥	≥	NOUN
ijassa-497	193	6	at}|	at}|	NOUN
ijassa-497	193	7	.	.	PUNCT
ijassa-497	194	1	the	the	DET
ijassa-497	194	2	algorithm	algorithm	NOUN
ijassa-497	194	3	is	be	AUX
ijassa-497	194	4	depicted	depict	VERB
ijassa-497	194	5	in	in	ADP
ijassa-497	194	6	the	the	DET
ijassa-497	194	7	figure	figure	NOUN
ijassa-497	194	8	4.1	4.1	NUM
ijassa-497	194	9	.	.	PUNCT
ijassa-497	195	1	fig	fig	NOUN
ijassa-497	195	2	.	.	PUNCT
ijassa-497	196	1	4.1	4.1	NUM
ijassa-497	196	2	.	.	PUNCT
ijassa-497	196	3	flowchart	flowchart	NOUN
ijassa-497	196	4	of	of	ADP
ijassa-497	196	5	the	the	DET
ijassa-497	196	6	algorithm	algorithm	NOUN
ijassa-497	196	7	it	it	PRON
ijassa-497	196	8	is	be	AUX
ijassa-497	196	9	worth	worth	ADJ
ijassa-497	196	10	emphasising	emphasise	VERB
ijassa-497	196	11	that	that	SCONJ
ijassa-497	196	12	the	the	DET
ijassa-497	196	13	proposed	propose	VERB
ijassa-497	196	14	approach	approach	NOUN
ijassa-497	196	15	could	could	AUX
ijassa-497	196	16	be	be	AUX
ijassa-497	196	17	implemented	implement	VERB
ijassa-497	196	18	with	with	ADP
ijassa-497	196	19	time	time	NOUN
ijassa-497	196	20	complexity	complexity	NOUN
ijassa-497	196	21	equal	equal	ADJ
ijassa-497	196	22	to	to	ADP
ijassa-497	196	23	o(n	o(n	NUM
ijassa-497	196	24	)	)	PUNCT
ijassa-497	197	1	+	+	NOUN
ijassa-497	197	2	o(logm	o(logm	NOUN
ijassa-497	197	3	)	)	PUNCT
ijassa-497	197	4	in	in	ADP
ijassa-497	197	5	the	the	DET
ijassa-497	197	6	case	case	NOUN
ijassa-497	197	7	of	of	ADP
ijassa-497	197	8	using	use	VERB
ijassa-497	197	9	red	red	ADJ
ijassa-497	197	10	-	-	PUNCT
ijassa-497	197	11	black	black	ADJ
ijassa-497	197	12	tree	tree	NOUN
ijassa-497	197	13	for	for	ADP
ijassa-497	197	14	the	the	DET
ijassa-497	197	15	calibration	calibration	NOUN
ijassa-497	197	16	set	set	VERB
ijassa-497	197	17	.	.	PUNCT
ijassa-497	198	1	5	5	X
ijassa-497	198	2	.	.	X
ijassa-497	198	3	results	result	NOUN
ijassa-497	198	4	on	on	ADP
ijassa-497	198	5	numenta	numenta	PROPN
ijassa-497	198	6	anomaly	anomaly	NOUN
ijassa-497	198	7	benchmark	benchmark	NOUN
ijassa-497	198	8	the	the	DET
ijassa-497	198	9	results	result	NOUN
ijassa-497	198	10	of	of	ADP
ijassa-497	198	11	the	the	DET
ijassa-497	198	12	expose	expose	ADJ
ijassa-497	198	13	ldcd	ldcd	ADJ
ijassa-497	198	14	comparison	comparison	NOUN
ijassa-497	198	15	with	with	ADP
ijassa-497	198	16	several	several	ADJ
ijassa-497	198	17	other	other	ADJ
ijassa-497	198	18	algorithms	algorithm	NOUN
ijassa-497	198	19	is	be	AUX
ijassa-497	198	20	presented	present	VERB
ijassa-497	198	21	in	in	ADP
ijassa-497	198	22	this	this	DET
ijassa-497	198	23	section	section	NOUN
ijassa-497	198	24	along	along	ADP
ijassa-497	198	25	with	with	ADP
ijassa-497	198	26	the	the	DET
ijassa-497	198	27	testing	testing	NOUN
ijassa-497	198	28	methodology	methodology	NOUN
ijassa-497	198	29	.	.	PUNCT
ijassa-497	199	1	the	the	DET
ijassa-497	199	2	numenta	numenta	PROPN
ijassa-497	199	3	anomaly	anomaly	NOUN
ijassa-497	199	4	benchmark	benchmark	NOUN
ijassa-497	199	5	(	(	PUNCT
ijassa-497	199	6	nab	nab	NOUN
ijassa-497	199	7	)	)	PUNCT
ijassa-497	199	8	is	be	AUX
ijassa-497	199	9	utilized	utilize	VERB
ijassa-497	199	10	to	to	PART
ijassa-497	199	11	test	test	VERB
ijassa-497	199	12	the	the	DET
ijassa-497	199	13	proposed	propose	VERB
ijassa-497	199	14	algorithm	algorithm	NOUN
ijassa-497	199	15	.	.	PUNCT
ijassa-497	200	1	5.1	5.1	NUM
ijassa-497	200	2	.	.	PUNCT
ijassa-497	201	1	datasets	dataset	VERB
ijassa-497	201	2	the	the	DET
ijassa-497	201	3	nab	nab	ADJ
ijassa-497	201	4	corpus	corpus	NOUN
ijassa-497	201	5	consists	consist	VERB
ijassa-497	201	6	of	of	ADP
ijassa-497	201	7	58	58	NUM
ijassa-497	201	8	both	both	CCONJ
ijassa-497	201	9	real	real	ADJ
ijassa-497	201	10	-	-	PUNCT
ijassa-497	201	11	world	world	NOUN
ijassa-497	201	12	and	and	CCONJ
ijassa-497	201	13	artificial	artificial	ADJ
ijassa-497	201	14	time	time	NOUN
ijassa-497	201	15	series	series	PROPN
ijassa-497	201	16	datasets	dataset	NOUN
ijassa-497	201	17	.	.	PUNCT
ijassa-497	202	1	real	real	ADJ
ijassa-497	202	2	-	-	PUNCT
ijassa-497	202	3	world	world	NOUN
ijassa-497	202	4	data	datum	NOUN
ijassa-497	202	5	are	be	AUX
ijassa-497	202	6	obtained	obtain	VERB
ijassa-497	202	7	from	from	ADP
ijassa-497	202	8	such	such	ADJ
ijassa-497	202	9	sources	source	NOUN
ijassa-497	202	10	as	as	ADP
ijassa-497	202	11	aws	aws	NOUN
ijassa-497	202	12	server	server	NOUN
ijassa-497	202	13	metrics	metric	NOUN
ijassa-497	202	14	,	,	PUNCT
ijassa-497	202	15	twitter	twitter	NOUN
ijassa-497	202	16	volume	volume	NOUN
ijassa-497	202	17	,	,	PUNCT
ijassa-497	202	18	advertisement	advertisement	NOUN
ijassa-497	202	19	clicking	click	VERB
ijassa-497	202	20	metrics	metric	NOUN
ijassa-497	202	21	,	,	PUNCT
ijassa-497	202	22	traffic	traffic	NOUN
ijassa-497	202	23	data	datum	NOUN
ijassa-497	202	24	,	,	PUNCT
ijassa-497	202	25	to	to	PART
ijassa-497	202	26	name	name	VERB
ijassa-497	202	27	just	just	ADV
ijassa-497	202	28	a	a	DET
ijassa-497	202	29	few	few	ADJ
ijassa-497	202	30	.	.	PUNCT
ijassa-497	203	1	we	we	PRON
ijassa-497	203	2	also	also	ADV
ijassa-497	203	3	conduct	conduct	VERB
ijassa-497	203	4	experiments	experiment	NOUN
ijassa-497	203	5	using	use	VERB
ijassa-497	203	6	numenta	numenta	PROPN
ijassa-497	203	7	anomaly	anomaly	NOUN
ijassa-497	203	8	benchmark	benchmark	NOUN
ijassa-497	203	9	on	on	ADP
ijassa-497	203	10	yahoo	yahoo	PROPN
ijassa-497	203	11	!	!	PUNCT
ijassa-497	203	12	s5	s5	PROPN
ijassa-497	203	13	dataset	dataset	VERB
ijassa-497	204	1	[	[	X
ijassa-497	204	2	34	34	NUM
ijassa-497	204	3	]	]	PUNCT
ijassa-497	204	4	which	which	PRON
ijassa-497	204	5	has	have	AUX
ijassa-497	204	6	been	be	AUX
ijassa-497	204	7	created	create	VERB
ijassa-497	204	8	to	to	PART
ijassa-497	204	9	gauge	gauge	VERB
ijassa-497	204	10	the	the	DET
ijassa-497	204	11	anomaly	anomaly	NOUN
ijassa-497	204	12	detectors	detector	NOUN
ijassa-497	204	13	performance	performance	NOUN
ijassa-497	204	14	on	on	ADP
ijassa-497	204	15	different	different	ADJ
ijassa-497	204	16	types	type	NOUN
ijassa-497	204	17	of	of	ADP
ijassa-497	204	18	anomalies	anomaly	NOUN
ijassa-497	204	19	.	.	PUNCT
ijassa-497	205	1	this	this	DET
ijassa-497	205	2	corpus	corpus	NOUN
ijassa-497	205	3	is	be	AUX
ijassa-497	205	4	divided	divide	VERB
ijassa-497	205	5	into	into	ADP
ijassa-497	205	6	4	4	NUM
ijassa-497	205	7	groups	group	NOUN
ijassa-497	205	8	:	:	PUNCT
ijassa-497	205	9	first	first	ADJ
ijassa-497	205	10	one	one	NOUN
ijassa-497	205	11	contains	contain	VERB
ijassa-497	205	12	real	real	ADJ
ijassa-497	205	13	production	production	NOUN
ijassa-497	205	14	metrics	metric	NOUN
ijassa-497	205	15	from	from	ADP
ijassa-497	205	16	different	different	ADJ
ijassa-497	205	17	yahoo	yahoo	PROPN
ijassa-497	205	18	!	!	PUNCT
ijassa-497	205	19	properties	property	NOUN
ijassa-497	205	20	,	,	PUNCT
ijassa-497	205	21	and	and	CCONJ
ijassa-497	205	22	the	the	DET
ijassa-497	205	23	rest	rest	NOUN
ijassa-497	205	24	are	be	AUX
ijassa-497	205	25	synthetic	synthetic	ADJ
ijassa-497	205	26	time	time	NOUN
ijassa-497	205	27	series	series	NOUN
ijassa-497	205	28	.	.	PUNCT
ijassa-497	206	1	copyright	copyright	NOUN
ijassa-497	206	2	©	©	PROPN
ijassa-497	206	3	2017	2017	NUM
ijassa-497	206	4	assa	assa	NOUN
ijassa-497	206	5	.	.	PUNCT
ijassa-497	207	1	adv	adv	PROPN
ijassa-497	207	2	syst	syst	PROPN
ijassa-497	207	3	sci	sci	PROPN
ijassa-497	207	4	appl	appl	PROPN
ijassa-497	207	5	(	(	PUNCT
ijassa-497	207	6	2017	2017	NUM
ijassa-497	207	7	)	)	PUNCT
ijassa-497	207	8	conformal	conformal	ADJ
ijassa-497	207	9	kernel	kernel	NOUN
ijassa-497	207	10	expected	expect	VERB
ijassa-497	207	11	similarity	similarity	NOUN
ijassa-497	207	12	for	for	ADP
ijassa-497	207	13	anomaly	anomaly	NOUN
ijassa-497	207	14	detection	detection	NOUN
ijassa-497	207	15	in	in	ADP
ijassa-497	207	16	time	time	NOUN
ijassa-497	207	17	-	-	PUNCT
ijassa-497	207	18	series	series	NOUN
ijassa-497	207	19	data	datum	NOUN
ijassa-497	207	20	29	29	NUM
ijassa-497	207	21	5.2	5.2	NUM
ijassa-497	207	22	.	.	PUNCT
ijassa-497	208	1	scoring	score	VERB
ijassa-497	208	2	algorithm	algorithm	NOUN
ijassa-497	208	3	commonly	commonly	ADV
ijassa-497	208	4	used	use	VERB
ijassa-497	208	5	metrics	metric	NOUN
ijassa-497	208	6	for	for	ADP
ijassa-497	208	7	performance	performance	NOUN
ijassa-497	208	8	evaluation	evaluation	NOUN
ijassa-497	208	9	such	such	ADJ
ijassa-497	208	10	as	as	ADP
ijassa-497	208	11	accuracy	accuracy	NOUN
ijassa-497	208	12	,	,	PUNCT
ijassa-497	208	13	precision	precision	NOUN
ijassa-497	208	14	and	and	CCONJ
ijassa-497	208	15	recall	recall	NOUN
ijassa-497	208	16	do	do	AUX
ijassa-497	208	17	not	not	PART
ijassa-497	208	18	suit	suit	VERB
ijassa-497	208	19	well	well	ADV
ijassa-497	208	20	for	for	ADP
ijassa-497	208	21	anomaly	anomaly	NOUN
ijassa-497	208	22	detection	detection	NOUN
ijassa-497	208	23	,	,	PUNCT
ijassa-497	208	24	since	since	SCONJ
ijassa-497	208	25	they	they	PRON
ijassa-497	208	26	do	do	AUX
ijassa-497	208	27	not	not	PART
ijassa-497	208	28	consider	consider	VERB
ijassa-497	208	29	time	time	NOUN
ijassa-497	208	30	.	.	PUNCT
ijassa-497	209	1	the	the	DET
ijassa-497	209	2	nab	nab	NOUN
ijassa-497	209	3	proposes	propose	VERB
ijassa-497	209	4	such	such	DET
ijassa-497	209	5	an	an	DET
ijassa-497	209	6	approach	approach	NOUN
ijassa-497	209	7	for	for	ADP
ijassa-497	209	8	scoring	scoring	NOUN
ijassa-497	209	9	which	which	PRON
ijassa-497	209	10	rewards	reward	VERB
ijassa-497	209	11	only	only	ADV
ijassa-497	209	12	early	early	ADJ
ijassa-497	209	13	true	true	ADJ
ijassa-497	209	14	detection	detection	NOUN
ijassa-497	209	15	,	,	PUNCT
ijassa-497	209	16	meanwhile	meanwhile	ADV
ijassa-497	209	17	penalizes	penalize	VERB
ijassa-497	209	18	late	late	ADJ
ijassa-497	209	19	detections	detection	NOUN
ijassa-497	209	20	and	and	CCONJ
ijassa-497	209	21	punishes	punish	VERB
ijassa-497	209	22	false	false	ADJ
ijassa-497	209	23	alarms	alarm	NOUN
ijassa-497	209	24	very	very	ADV
ijassa-497	209	25	hard	hard	ADV
ijassa-497	209	26	.	.	PUNCT
ijassa-497	210	1	to	to	PART
ijassa-497	210	2	capture	capture	VERB
ijassa-497	210	3	early	early	ADJ
ijassa-497	210	4	detections	detection	NOUN
ijassa-497	210	5	,	,	PUNCT
ijassa-497	210	6	nab	nab	NOUN
ijassa-497	210	7	considers	consider	VERB
ijassa-497	210	8	the	the	DET
ijassa-497	210	9	area	area	NOUN
ijassa-497	210	10	which	which	PRON
ijassa-497	210	11	is	be	AUX
ijassa-497	210	12	centred	centre	VERB
ijassa-497	210	13	around	around	ADP
ijassa-497	210	14	the	the	DET
ijassa-497	210	15	anomaly	anomaly	NOUN
ijassa-497	210	16	point	point	NOUN
ijassa-497	210	17	which	which	PRON
ijassa-497	210	18	is	be	AUX
ijassa-497	210	19	referred	refer	VERB
ijassa-497	210	20	to	to	ADP
ijassa-497	210	21	as	as	ADP
ijassa-497	210	22	anomaly	anomaly	NOUN
ijassa-497	210	23	window	window	NOUN
ijassa-497	210	24	.	.	PUNCT
ijassa-497	211	1	the	the	DET
ijassa-497	211	2	window	window	NOUN
ijassa-497	211	3	length	length	NOUN
ijassa-497	211	4	is	be	AUX
ijassa-497	211	5	defined	define	VERB
ijassa-497	211	6	as	as	ADP
ijassa-497	211	7	10	10	NUM
ijassa-497	211	8	%	%	NOUN
ijassa-497	211	9	of	of	ADP
ijassa-497	211	10	the	the	DET
ijassa-497	211	11	length	length	NOUN
ijassa-497	211	12	of	of	ADP
ijassa-497	211	13	the	the	DET
ijassa-497	211	14	time	time	NOUN
ijassa-497	211	15	series	series	NOUN
ijassa-497	211	16	.	.	PUNCT
ijassa-497	212	1	all	all	DET
ijassa-497	212	2	detections	detection	NOUN
ijassa-497	212	3	within	within	ADP
ijassa-497	212	4	this	this	DET
ijassa-497	212	5	window	window	NOUN
ijassa-497	212	6	are	be	AUX
ijassa-497	212	7	true	true	ADJ
ijassa-497	212	8	positives	positive	NOUN
ijassa-497	212	9	,	,	PUNCT
ijassa-497	212	10	but	but	CCONJ
ijassa-497	212	11	only	only	ADV
ijassa-497	212	12	the	the	DET
ijassa-497	212	13	earliest	early	ADJ
ijassa-497	212	14	one	one	NUM
ijassa-497	212	15	contributes	contribute	VERB
ijassa-497	212	16	in	in	ADP
ijassa-497	212	17	the	the	DET
ijassa-497	212	18	total	total	ADJ
ijassa-497	212	19	score	score	NOUN
ijassa-497	212	20	,	,	PUNCT
ijassa-497	212	21	the	the	DET
ijassa-497	212	22	others	other	NOUN
ijassa-497	212	23	will	will	AUX
ijassa-497	212	24	be	be	AUX
ijassa-497	212	25	ignored	ignore	VERB
ijassa-497	212	26	.	.	PUNCT
ijassa-497	213	1	the	the	DET
ijassa-497	213	2	detections	detection	NOUN
ijassa-497	213	3	outside	outside	ADP
ijassa-497	213	4	the	the	DET
ijassa-497	213	5	anomaly	anomaly	NOUN
ijassa-497	213	6	window	window	NOUN
ijassa-497	213	7	are	be	AUX
ijassa-497	213	8	false	false	ADJ
ijassa-497	213	9	positives	positive	NOUN
ijassa-497	213	10	,	,	PUNCT
ijassa-497	213	11	missed	miss	VERB
ijassa-497	213	12	anomalies	anomaly	NOUN
ijassa-497	213	13	are	be	AUX
ijassa-497	213	14	false	false	ADJ
ijassa-497	213	15	negatives	negative	NOUN
ijassa-497	213	16	.	.	PUNCT
ijassa-497	214	1	true	true	ADJ
ijassa-497	214	2	negatives	negative	NOUN
ijassa-497	214	3	are	be	AUX
ijassa-497	214	4	not	not	PART
ijassa-497	214	5	considered	consider	VERB
ijassa-497	214	6	in	in	ADP
ijassa-497	214	7	the	the	DET
ijassa-497	214	8	scoring	scoring	NOUN
ijassa-497	214	9	mechanism	mechanism	NOUN
ijassa-497	214	10	.	.	PUNCT
ijassa-497	215	1	bellow	bellow	ADJ
ijassa-497	215	2	we	we	PRON
ijassa-497	215	3	described	describe	VERB
ijassa-497	215	4	scoring	scoring	NOUN
ijassa-497	215	5	scheme	scheme	NOUN
ijassa-497	215	6	used	use	VERB
ijassa-497	215	7	in	in	ADP
ijassa-497	215	8	nab	nab	NOUN
ijassa-497	215	9	[	[	X
ijassa-497	215	10	33	33	NUM
ijassa-497	215	11	]	]	PUNCT
ijassa-497	215	12	.	.	PUNCT
ijassa-497	216	1	an	an	DET
ijassa-497	216	2	example	example	NOUN
ijassa-497	216	3	of	of	ADP
ijassa-497	216	4	time	time	NOUN
ijassa-497	216	5	series	series	NOUN
ijassa-497	216	6	is	be	AUX
ijassa-497	216	7	provided	provide	VERB
ijassa-497	216	8	in	in	ADP
ijassa-497	216	9	figure	figure	NOUN
ijassa-497	216	10	5.2	5.2	NUM
ijassa-497	216	11	.	.	PUNCT
ijassa-497	217	1	the	the	DET
ijassa-497	217	2	first	first	ADJ
ijassa-497	217	3	15	15	NUM
ijassa-497	217	4	%	%	NOUN
ijassa-497	217	5	of	of	ADP
ijassa-497	217	6	the	the	DET
ijassa-497	217	7	time	time	NOUN
ijassa-497	217	8	series	series	NOUN
ijassa-497	217	9	is	be	AUX
ijassa-497	217	10	considered	consider	VERB
ijassa-497	217	11	as	as	ADP
ijassa-497	217	12	probationary	probationary	ADJ
ijassa-497	217	13	period	period	NOUN
ijassa-497	217	14	and	and	CCONJ
ijassa-497	217	15	during	during	ADP
ijassa-497	217	16	this	this	DET
ijassa-497	217	17	period	period	NOUN
ijassa-497	217	18	an	an	DET
ijassa-497	217	19	algorithm	algorithm	NOUN
ijassa-497	217	20	learns	learn	VERB
ijassa-497	217	21	patterns	pattern	NOUN
ijassa-497	217	22	from	from	ADP
ijassa-497	217	23	the	the	DET
ijassa-497	217	24	data	datum	NOUN
ijassa-497	217	25	and	and	CCONJ
ijassa-497	217	26	is	be	AUX
ijassa-497	217	27	not	not	PART
ijassa-497	217	28	required	require	VERB
ijassa-497	217	29	to	to	PART
ijassa-497	217	30	do	do	VERB
ijassa-497	217	31	any	any	DET
ijassa-497	217	32	detections	detection	NOUN
ijassa-497	217	33	.	.	PUNCT
ijassa-497	218	1	then	then	ADV
ijassa-497	218	2	the	the	DET
ijassa-497	218	3	algorithm	algorithm	NOUN
ijassa-497	218	4	is	be	AUX
ijassa-497	218	5	evaluated	evaluate	VERB
ijassa-497	218	6	on	on	ADP
ijassa-497	218	7	the	the	DET
ijassa-497	218	8	remaining	remain	VERB
ijassa-497	218	9	part	part	NOUN
ijassa-497	218	10	of	of	ADP
ijassa-497	218	11	time	time	NOUN
ijassa-497	218	12	series	series	NOUN
ijassa-497	218	13	.	.	PUNCT
ijassa-497	219	1	the	the	DET
ijassa-497	219	2	weights	weight	NOUN
ijassa-497	219	3	for	for	ADP
ijassa-497	219	4	accuracy	accuracy	NOUN
ijassa-497	219	5	calculation	calculation	NOUN
ijassa-497	219	6	is	be	AUX
ijassa-497	219	7	evaluated	evaluate	VERB
ijassa-497	219	8	using	use	VERB
ijassa-497	219	9	the	the	DET
ijassa-497	219	10	smooth	smooth	ADJ
ijassa-497	219	11	sigmoid	sigmoid	NOUN
ijassa-497	219	12	function	function	NOUN
ijassa-497	219	13	as	as	SCONJ
ijassa-497	219	14	depicted	depict	VERB
ijassa-497	219	15	in	in	ADP
ijassa-497	219	16	figure	figure	NOUN
ijassa-497	219	17	5.3	5.3	NUM
ijassa-497	219	18	.	.	PUNCT
ijassa-497	220	1	fig	fig	NOUN
ijassa-497	220	2	.	.	PUNCT
ijassa-497	221	1	5.2	5.2	NUM
ijassa-497	221	2	.	.	PUNCT
ijassa-497	222	1	the	the	DET
ijassa-497	222	2	purple	purple	ADJ
ijassa-497	222	3	shaded	shaded	ADJ
ijassa-497	222	4	area	area	NOUN
ijassa-497	222	5	is	be	AUX
ijassa-497	222	6	the	the	DET
ijassa-497	222	7	probationary	probationary	ADJ
ijassa-497	222	8	period	period	NOUN
ijassa-497	222	9	.	.	PUNCT
ijassa-497	223	1	anomalies	anomaly	NOUN
ijassa-497	223	2	are	be	AUX
ijassa-497	223	3	depicted	depict	VERB
ijassa-497	223	4	as	as	ADP
ijassa-497	223	5	red	red	ADJ
ijassa-497	223	6	points	point	NOUN
ijassa-497	223	7	and	and	CCONJ
ijassa-497	223	8	red	red	ADJ
ijassa-497	223	9	shaded	shade	VERB
ijassa-497	223	10	regions	region	NOUN
ijassa-497	223	11	represent	represent	VERB
ijassa-497	223	12	anomaly	anomaly	NOUN
ijassa-497	223	13	windows	window	NOUN
ijassa-497	224	1	[	[	X
ijassa-497	224	2	33	33	NUM
ijassa-497	224	3	]	]	PUNCT
ijassa-497	224	4	.	.	PUNCT
ijassa-497	225	1	as	as	ADP
ijassa-497	225	2	the	the	DET
ijassa-497	225	3	costs	cost	NOUN
ijassa-497	225	4	of	of	ADP
ijassa-497	225	5	true	true	ADJ
ijassa-497	225	6	positive	positive	ADJ
ijassa-497	225	7	(	(	PUNCT
ijassa-497	225	8	tp	tp	NOUN
ijassa-497	225	9	)	)	PUNCT
ijassa-497	225	10	,	,	PUNCT
ijassa-497	225	11	false	false	ADJ
ijassa-497	225	12	positive	positive	ADJ
ijassa-497	225	13	(	(	PUNCT
ijassa-497	225	14	fp	fp	NOUN
ijassa-497	225	15	)	)	PUNCT
ijassa-497	225	16	and	and	CCONJ
ijassa-497	225	17	false	false	ADJ
ijassa-497	225	18	negative	negative	ADJ
ijassa-497	225	19	(	(	PUNCT
ijassa-497	225	20	fn	fn	NOUN
ijassa-497	225	21	)	)	PUNCT
ijassa-497	225	22	vary	vary	VERB
ijassa-497	225	23	among	among	ADP
ijassa-497	225	24	distinct	distinct	ADJ
ijassa-497	225	25	applications	application	NOUN
ijassa-497	225	26	,	,	PUNCT
ijassa-497	225	27	in	in	ADP
ijassa-497	225	28	nab	nab	NOUN
ijassa-497	225	29	this	this	PRON
ijassa-497	225	30	is	be	AUX
ijassa-497	225	31	captured	capture	VERB
ijassa-497	225	32	by	by	ADP
ijassa-497	225	33	an	an	DET
ijassa-497	225	34	application	application	NOUN
ijassa-497	225	35	profile	profile	NOUN
ijassa-497	225	36	which	which	PRON
ijassa-497	225	37	reflects	reflect	VERB
ijassa-497	225	38	the	the	DET
ijassa-497	225	39	contribution	contribution	NOUN
ijassa-497	225	40	of	of	ADP
ijassa-497	225	41	weights	weight	NOUN
ijassa-497	225	42	for	for	ADP
ijassa-497	225	43	tp	tp	NOUN
ijassa-497	225	44	,	,	PUNCT
ijassa-497	225	45	fp	fp	PROPN
ijassa-497	225	46	an	an	DET
ijassa-497	225	47	fn	fn	NOUN
ijassa-497	225	48	detections	detection	NOUN
ijassa-497	225	49	.	.	PUNCT
ijassa-497	226	1	the	the	DET
ijassa-497	226	2	“	"	PUNCT
ijassa-497	226	3	standard	standard	ADJ
ijassa-497	226	4	”	"	PUNCT
ijassa-497	226	5	application	application	NOUN
ijassa-497	226	6	profile	profile	NOUN
ijassa-497	226	7	reflects	reflect	VERB
ijassa-497	226	8	scenarios	scenario	NOUN
ijassa-497	226	9	in	in	ADP
ijassa-497	226	10	which	which	PRON
ijassa-497	226	11	misdetections	misdetection	NOUN
ijassa-497	226	12	have	have	VERB
ijassa-497	226	13	identical	identical	ADJ
ijassa-497	226	14	costs	cost	NOUN
ijassa-497	226	15	.	.	PUNCT
ijassa-497	227	1	the	the	DET
ijassa-497	227	2	“	"	PUNCT
ijassa-497	227	3	reward	reward	VERB
ijassa-497	227	4	low	low	PROPN
ijassa-497	227	5	fp	fp	X
ijassa-497	227	6	”	"	PUNCT
ijassa-497	227	7	and	and	CCONJ
ijassa-497	227	8	“	"	PUNCT
ijassa-497	227	9	reward	reward	VERB
ijassa-497	227	10	low	low	ADJ
ijassa-497	227	11	fn	fn	NOUN
ijassa-497	227	12	”	"	PUNCT
ijassa-497	227	13	profiles	profile	NOUN
ijassa-497	227	14	penalize	penalize	VERB
ijassa-497	227	15	harder	hard	ADV
ijassa-497	227	16	for	for	ADP
ijassa-497	227	17	fp	fp	NOUN
ijassa-497	227	18	and	and	CCONJ
ijassa-497	227	19	fn	fn	NOUN
ijassa-497	227	20	respectively	respectively	ADV
ijassa-497	227	21	.	.	PUNCT
ijassa-497	228	1	profile	profile	PROPN
ijassa-497	228	2	atp	atp	PROPN
ijassa-497	228	3	afn	afn	PROPN
ijassa-497	228	4	afp	afp	PROPN
ijassa-497	228	5	atn	atn	PROPN
ijassa-497	228	6	standard	standard	PROPN
ijassa-497	228	7	1.0	1.0	NUM
ijassa-497	228	8	-1.0	-1.0	PROPN
ijassa-497	228	9	-0.11	-0.11	NUM
ijassa-497	228	10	1.0	1.0	NUM
ijassa-497	228	11	reward	reward	NOUN
ijassa-497	228	12	low	low	ADJ
ijassa-497	228	13	fp	fp	PROPN
ijassa-497	228	14	1.0	1.0	NUM
ijassa-497	228	15	-1.0	-1.0	PROPN
ijassa-497	228	16	-0.22	-0.22	NUM
ijassa-497	228	17	1.0	1.0	NUM
ijassa-497	228	18	reward	reward	NOUN
ijassa-497	228	19	low	low	ADJ
ijassa-497	228	20	fn	fn	PROPN
ijassa-497	228	21	1.0	1.0	NUM
ijassa-497	228	22	-2.0	-2.0	PROPN
ijassa-497	228	23	-0.11	-0.11	NUM
ijassa-497	228	24	1.0	1.0	NUM
ijassa-497	228	25	table	table	NOUN
ijassa-497	228	26	5.1	5.1	NUM
ijassa-497	228	27	.	.	PUNCT
ijassa-497	229	1	the	the	DET
ijassa-497	229	2	detection	detection	NOUN
ijassa-497	229	3	rewards	reward	VERB
ijassa-497	229	4	on	on	ADP
ijassa-497	229	5	nab	nab	ADJ
ijassa-497	229	6	application	application	NOUN
ijassa-497	229	7	profiles	profile	NOUN
ijassa-497	229	8	the	the	DET
ijassa-497	229	9	reward	reward	NOUN
ijassa-497	229	10	for	for	ADP
ijassa-497	229	11	the	the	DET
ijassa-497	229	12	detection	detection	NOUN
ijassa-497	229	13	depends	depend	VERB
ijassa-497	229	14	on	on	ADP
ijassa-497	229	15	the	the	DET
ijassa-497	229	16	relative	relative	ADJ
ijassa-497	229	17	position	position	NOUN
ijassa-497	229	18	t	t	PROPN
ijassa-497	229	19	of	of	ADP
ijassa-497	229	20	the	the	DET
ijassa-497	229	21	alarm	alarm	NOUN
ijassa-497	229	22	(	(	PUNCT
ijassa-497	229	23	about	about	ADP
ijassa-497	229	24	possible	possible	ADJ
ijassa-497	229	25	anomaly	anomaly	NOUN
ijassa-497	229	26	)	)	PUNCT
ijassa-497	229	27	to	to	ADP
ijassa-497	229	28	the	the	DET
ijassa-497	229	29	left	left	ADJ
ijassa-497	229	30	side	side	NOUN
ijassa-497	229	31	of	of	ADP
ijassa-497	229	32	the	the	DET
ijassa-497	229	33	anomaly	anomaly	NOUN
ijassa-497	229	34	window	window	NOUN
ijassa-497	229	35	:	:	PUNCT
ijassa-497	229	36	σa(t	σa(t	X
ijassa-497	229	37	)	)	PUNCT
ijassa-497	229	38	=	=	PRON
ijassa-497	229	39	(	(	PUNCT
ijassa-497	229	40	atp	atp	PROPN
ijassa-497	229	41	−	−	PROPN
ijassa-497	229	42	afp	afp	PROPN
ijassa-497	229	43	)	)	PUNCT
ijassa-497	229	44	(	(	PUNCT
ijassa-497	229	45	1	1	NUM
ijassa-497	229	46	1	1	NUM
ijassa-497	229	47	+	+	NUM
ijassa-497	229	48	e5	e5	PROPN
ijassa-497	229	49	t	t	PROPN
ijassa-497	229	50	)	)	PUNCT
ijassa-497	229	51	−	−	PROPN
ijassa-497	230	1	1	1	X
ijassa-497	230	2	.	.	PUNCT
ijassa-497	231	1	the	the	DET
ijassa-497	231	2	raw	raw	ADJ
ijassa-497	231	3	performance	performance	NOUN
ijassa-497	231	4	score	score	NOUN
ijassa-497	231	5	on	on	ADP
ijassa-497	231	6	the	the	DET
ijassa-497	231	7	dataset	dataset	NOUN
ijassa-497	231	8	x	x	PUNCT
ijassa-497	231	9	with	with	ADP
ijassa-497	231	10	respect	respect	NOUN
ijassa-497	231	11	to	to	ADP
ijassa-497	231	12	application	application	NOUN
ijassa-497	231	13	profile	profile	NOUN
ijassa-497	231	14	a	a	PRON
ijassa-497	231	15	is	be	AUX
ijassa-497	231	16	the	the	DET
ijassa-497	231	17	sum	sum	NOUN
ijassa-497	231	18	of	of	ADP
ijassa-497	231	19	the	the	DET
ijassa-497	231	20	scores	score	NOUN
ijassa-497	231	21	over	over	ADP
ijassa-497	231	22	all	all	DET
ijassa-497	231	23	detections	detection	NOUN
ijassa-497	231	24	plus	plus	CCONJ
ijassa-497	231	25	the	the	DET
ijassa-497	231	26	impact	impact	NOUN
ijassa-497	231	27	of	of	ADP
ijassa-497	231	28	missed	miss	VERB
ijassa-497	231	29	anomalies	anomaly	NOUN
ijassa-497	231	30	(	(	PUNCT
ijassa-497	231	31	false	false	ADJ
ijassa-497	231	32	negatives	negative	NOUN
ijassa-497	231	33	)	)	PUNCT
ijassa-497	231	34	copyright	copyright	NOUN
ijassa-497	231	35	©	©	PROPN
ijassa-497	231	36	2017	2017	NUM
ijassa-497	231	37	assa	assa	NOUN
ijassa-497	231	38	.	.	PUNCT
ijassa-497	232	1	adv	adv	PROPN
ijassa-497	232	2	syst	syst	PROPN
ijassa-497	232	3	sci	sci	PROPN
ijassa-497	232	4	appl	appl	PROPN
ijassa-497	232	5	(	(	PUNCT
ijassa-497	232	6	2017	2017	NUM
ijassa-497	232	7	)	)	PUNCT
ijassa-497	232	8	30	30	NUM
ijassa-497	232	9	aleksandr	aleksandr	PROPN
ijassa-497	232	10	safin	safin	PROPN
ijassa-497	232	11	,	,	PUNCT
ijassa-497	232	12	evgeny	evgeny	PROPN
ijassa-497	232	13	burnaev	burnaev	PROPN
ijassa-497	232	14	captured	capture	VERB
ijassa-497	232	15	by	by	ADP
ijassa-497	232	16	the	the	DET
ijassa-497	232	17	number	number	NOUN
ijassa-497	232	18	of	of	ADP
ijassa-497	232	19	anomaly	anomaly	NOUN
ijassa-497	232	20	windows	window	NOUN
ijassa-497	232	21	with	with	ADP
ijassa-497	232	22	no	no	DET
ijassa-497	232	23	detections	detection	NOUN
ijassa-497	232	24	fdet	fdet	NOUN
ijassa-497	232	25	:	:	PUNCT
ijassa-497	232	26	sadet(x	sadet(x	NUM
ijassa-497	232	27	)	)	PUNCT
ijassa-497	232	28	=	=	PUNCT
ijassa-497	232	29	∑	∑	PUNCT
ijassa-497	232	30	y∈ydet	y∈ydet	PROPN
ijassa-497	232	31	σa(y	σa(y	NOUN
ijassa-497	232	32	)	)	PUNCT
ijassa-497	232	33	+	+	CCONJ
ijassa-497	232	34	afnfdet	afnfdet	NOUN
ijassa-497	232	35	.	.	PUNCT
ijassa-497	233	1	the	the	DET
ijassa-497	233	2	overall	overall	ADJ
ijassa-497	233	3	performance	performance	NOUN
ijassa-497	233	4	of	of	ADP
ijassa-497	233	5	the	the	DET
ijassa-497	233	6	algorithm	algorithm	NOUN
ijassa-497	233	7	is	be	AUX
ijassa-497	233	8	the	the	DET
ijassa-497	233	9	sum	sum	NOUN
ijassa-497	233	10	of	of	ADP
ijassa-497	233	11	raw	raw	ADJ
ijassa-497	233	12	performance	performance	NOUN
ijassa-497	233	13	scores	score	NOUN
ijassa-497	233	14	over	over	ADP
ijassa-497	233	15	the	the	DET
ijassa-497	233	16	all	all	DET
ijassa-497	233	17	datasets	dataset	NOUN
ijassa-497	233	18	d	d	NOUN
ijassa-497	233	19	:	:	PUNCT
ijassa-497	233	20	sadet	sadet	NOUN
ijassa-497	233	21	=	=	PUNCT
ijassa-497	233	22	∑	∑	PROPN
ijassa-497	233	23	x∈d	x∈d	PROPN
ijassa-497	233	24	s	s	PART
ijassa-497	233	25	a	a	DET
ijassa-497	233	26	det(x	det(x	PROPN
ijassa-497	233	27	)	)	PUNCT
ijassa-497	233	28	.	.	PUNCT
ijassa-497	234	1	the	the	DET
ijassa-497	234	2	final	final	ADJ
ijassa-497	234	3	normalized	normalize	VERB
ijassa-497	234	4	performance	performance	NOUN
ijassa-497	234	5	score	score	NOUN
ijassa-497	234	6	is	be	AUX
ijassa-497	234	7	determined	determine	VERB
ijassa-497	234	8	by	by	ADP
ijassa-497	234	9	:	:	PUNCT
ijassa-497	234	10	sanab	sanab	NOUN
ijassa-497	234	11	=	=	SYM
ijassa-497	234	12	100	100	NUM
ijassa-497	234	13	sadet	sadet	NOUN
ijassa-497	234	14	−	−	PROPN
ijassa-497	234	15	sanull	sanull	NOUN
ijassa-497	234	16	saperfect	saperfect	NOUN
ijassa-497	234	17	−	−	PROPN
ijassa-497	234	18	sanull	sanull	NOUN
ijassa-497	234	19	.	.	PUNCT
ijassa-497	235	1	fig	fig	NOUN
ijassa-497	235	2	.	.	PUNCT
ijassa-497	236	1	5.3	5.3	NUM
ijassa-497	236	2	.	.	PUNCT
ijassa-497	237	1	nab	nab	PROPN
ijassa-497	237	2	weighted	weight	VERB
ijassa-497	237	3	scores	score	NOUN
ijassa-497	237	4	:	:	PUNCT
ijassa-497	237	5	detections	detection	NOUN
ijassa-497	237	6	outside	outside	ADP
ijassa-497	237	7	the	the	DET
ijassa-497	237	8	anomaly	anomaly	NOUN
ijassa-497	237	9	window	window	NOUN
ijassa-497	237	10	are	be	AUX
ijassa-497	237	11	false	false	ADJ
ijassa-497	237	12	positives	positive	NOUN
ijassa-497	237	13	and	and	CCONJ
ijassa-497	237	14	punished	punish	VERB
ijassa-497	237	15	;	;	PUNCT
ijassa-497	237	16	only	only	ADV
ijassa-497	237	17	earliest	early	ADJ
ijassa-497	237	18	detection	detection	NOUN
ijassa-497	237	19	inside	inside	ADP
ijassa-497	237	20	window	window	NOUN
ijassa-497	237	21	is	be	AUX
ijassa-497	237	22	true	true	ADJ
ijassa-497	237	23	positive	positive	ADJ
ijassa-497	237	24	and	and	CCONJ
ijassa-497	237	25	it	it	PRON
ijassa-497	237	26	will	will	AUX
ijassa-497	237	27	be	be	AUX
ijassa-497	237	28	counted	count	VERB
ijassa-497	237	29	,	,	PUNCT
ijassa-497	237	30	other	other	ADJ
ijassa-497	237	31	will	will	AUX
ijassa-497	237	32	be	be	AUX
ijassa-497	237	33	ignored	ignore	VERB
ijassa-497	237	34	[	[	PUNCT
ijassa-497	237	35	33	33	NUM
ijassa-497	237	36	]	]	PUNCT
ijassa-497	237	37	.	.	PUNCT
ijassa-497	238	1	5.3	5.3	NUM
ijassa-497	238	2	.	.	PUNCT
ijassa-497	238	3	results	result	NOUN
ijassa-497	238	4	since	since	SCONJ
ijassa-497	238	5	proposed	propose	VERB
ijassa-497	238	6	expose	expose	NOUN
ijassa-497	238	7	ldcd	ldcd	NOUN
ijassa-497	238	8	is	be	AUX
ijassa-497	238	9	conservative	conservative	ADJ
ijassa-497	238	10	and	and	CCONJ
ijassa-497	238	11	demonstrates	demonstrate	VERB
ijassa-497	238	12	high	high	ADJ
ijassa-497	238	13	level	level	NOUN
ijassa-497	238	14	of	of	ADP
ijassa-497	238	15	false	false	ADJ
ijassa-497	238	16	alarms	alarm	NOUN
ijassa-497	238	17	,	,	PUNCT
ijassa-497	238	18	we	we	PRON
ijassa-497	238	19	have	have	AUX
ijassa-497	238	20	applied	apply	VERB
ijassa-497	238	21	the	the	DET
ijassa-497	238	22	following	follow	VERB
ijassa-497	238	23	simple	simple	ADJ
ijassa-497	238	24	pruning	pruning	NOUN
ijassa-497	238	25	strategy	strategy	NOUN
ijassa-497	238	26	to	to	PART
ijassa-497	238	27	reduce	reduce	VERB
ijassa-497	238	28	the	the	DET
ijassa-497	238	29	false	false	ADJ
ijassa-497	238	30	alarm	alarm	NOUN
ijassa-497	238	31	rate	rate	NOUN
ijassa-497	238	32	:	:	PUNCT
ijassa-497	238	33	we	we	PRON
ijassa-497	238	34	output	output	VERB
ijassa-497	238	35	1−	1−	NUM
ijassa-497	238	36	p	p	NOUN
ijassa-497	238	37	as	as	ADP
ijassa-497	238	38	anomaly	anomaly	NOUN
ijassa-497	238	39	score	score	NOUN
ijassa-497	238	40	for	for	ADP
ijassa-497	238	41	the	the	DET
ijassa-497	238	42	observation	observation	NOUN
ijassa-497	238	43	xt	xt	ADP
ijassa-497	239	1	and	and	CCONJ
ijassa-497	239	2	if	if	SCONJ
ijassa-497	239	3	p	p	NOUN
ijassa-497	239	4	is	be	AUX
ijassa-497	239	5	greater	great	ADJ
ijassa-497	239	6	than	than	ADP
ijassa-497	239	7	99.65	99.65	NUM
ijassa-497	239	8	%	%	NOUN
ijassa-497	239	9	,	,	PUNCT
ijassa-497	239	10	then	then	ADV
ijassa-497	239	11	output	output	NOUN
ijassa-497	239	12	of	of	ADP
ijassa-497	239	13	the	the	DET
ijassa-497	239	14	detector	detector	NOUN
ijassa-497	239	15	is	be	AUX
ijassa-497	239	16	fixed	fix	VERB
ijassa-497	239	17	at	at	ADP
ijassa-497	239	18	0.5	0.5	NUM
ijassa-497	239	19	for	for	ADP
ijassa-497	239	20	the	the	DET
ijassa-497	239	21	next	next	ADJ
ijassa-497	239	22	n	n	PROPN
ijassa-497	239	23	5	5	NUM
ijassa-497	239	24	observations	observation	NOUN
ijassa-497	239	25	(	(	PUNCT
ijassa-497	239	26	n	n	X
ijassa-497	239	27	is	be	AUX
ijassa-497	239	28	the	the	DET
ijassa-497	239	29	length	length	NOUN
ijassa-497	239	30	of	of	ADP
ijassa-497	239	31	probationary	probationary	ADJ
ijassa-497	239	32	period	period	NOUN
ijassa-497	239	33	)	)	PUNCT
ijassa-497	239	34	.	.	PUNCT
ijassa-497	240	1	the	the	DET
ijassa-497	240	2	proposed	propose	VERB
ijassa-497	240	3	approach	approach	NOUN
ijassa-497	240	4	has	have	AUX
ijassa-497	240	5	been	be	AUX
ijassa-497	240	6	validated	validate	VERB
ijassa-497	240	7	on	on	ADP
ijassa-497	240	8	both	both	CCONJ
ijassa-497	240	9	the	the	DET
ijassa-497	240	10	numenta	numenta	PROPN
ijassa-497	240	11	anomaly	anomaly	NOUN
ijassa-497	240	12	benchmark	benchmark	NOUN
ijassa-497	240	13	corpus	corpus	NOUN
ijassa-497	240	14	and	and	CCONJ
ijassa-497	240	15	the	the	DET
ijassa-497	240	16	yahoo	yahoo	PROPN
ijassa-497	240	17	!	!	PUNCT
ijassa-497	240	18	s5	s5	PROPN
ijassa-497	240	19	dataset	dataset	VERB
ijassa-497	240	20	.	.	PUNCT
ijassa-497	241	1	tables	table	NOUN
ijassa-497	241	2	5.2	5.2	NUM
ijassa-497	241	3	and	and	CCONJ
ijassa-497	241	4	5.3	5.3	NUM
ijassa-497	241	5	reflect	reflect	VERB
ijassa-497	241	6	the	the	DET
ijassa-497	241	7	results	result	NOUN
ijassa-497	241	8	of	of	ADP
ijassa-497	241	9	the	the	DET
ijassa-497	241	10	algorithms	algorithms	NOUN
ijassa-497	241	11	comparison	comparison	NOUN
ijassa-497	241	12	.	.	PUNCT
ijassa-497	242	1	5.4	5.4	NUM
ijassa-497	242	2	.	.	PUNCT
ijassa-497	242	3	automated	automate	VERB
ijassa-497	242	4	kernel	kernel	NOUN
ijassa-497	242	5	bandwidth	bandwidth	NOUN
ijassa-497	242	6	tuning	tune	VERB
ijassa-497	242	7	kernel	kernel	NOUN
ijassa-497	242	8	-	-	PUNCT
ijassa-497	242	9	based	base	VERB
ijassa-497	242	10	methods	method	NOUN
ijassa-497	242	11	are	be	AUX
ijassa-497	242	12	sensitive	sensitive	ADJ
ijassa-497	242	13	to	to	ADP
ijassa-497	242	14	the	the	DET
ijassa-497	242	15	choice	choice	NOUN
ijassa-497	242	16	of	of	ADP
ijassa-497	242	17	bandwidth	bandwidth	NOUN
ijassa-497	242	18	,	,	PUNCT
ijassa-497	242	19	therefore	therefore	ADV
ijassa-497	242	20	we	we	PRON
ijassa-497	242	21	modify	modify	VERB
ijassa-497	242	22	the	the	DET
ijassa-497	242	23	algorithm	algorithm	NOUN
ijassa-497	242	24	to	to	PART
ijassa-497	242	25	choose	choose	VERB
ijassa-497	242	26	the	the	DET
ijassa-497	242	27	bandwidth	bandwidth	NOUN
ijassa-497	242	28	of	of	ADP
ijassa-497	242	29	the	the	DET
ijassa-497	242	30	kernel	kernel	NOUN
ijassa-497	242	31	based	base	VERB
ijassa-497	242	32	on	on	ADP
ijassa-497	242	33	the	the	DET
ijassa-497	242	34	best	good	ADJ
ijassa-497	242	35	value	value	NOUN
ijassa-497	242	36	of	of	ADP
ijassa-497	242	37	the	the	DET
ijassa-497	242	38	bandwidth	bandwidth	NOUN
ijassa-497	242	39	for	for	ADP
ijassa-497	242	40	kernel	kernel	PROPN
ijassa-497	242	41	density	density	PROPN
ijassa-497	242	42	estimator	estimator	NOUN
ijassa-497	242	43	obtained	obtain	VERB
ijassa-497	242	44	by	by	ADP
ijassa-497	242	45	3	3	NUM
ijassa-497	242	46	-	-	ADJ
ijassa-497	242	47	fold	fold	ADJ
ijassa-497	242	48	cross	cross	NOUN
ijassa-497	242	49	-	-	NOUN
ijassa-497	242	50	validation	validation	NOUN
ijassa-497	242	51	.	.	PUNCT
ijassa-497	243	1	the	the	DET
ijassa-497	243	2	proposed	propose	VERB
ijassa-497	243	3	modification	modification	NOUN
ijassa-497	243	4	demonstrates	demonstrate	VERB
ijassa-497	243	5	significantly	significantly	ADV
ijassa-497	243	6	better	well	ADJ
ijassa-497	243	7	results	result	NOUN
ijassa-497	243	8	on	on	ADP
ijassa-497	243	9	nab	nab	ADJ
ijassa-497	243	10	dataset	dataset	NOUN
ijassa-497	243	11	and	and	CCONJ
ijassa-497	243	12	is	be	AUX
ijassa-497	243	13	able	able	ADJ
ijassa-497	243	14	to	to	PART
ijassa-497	243	15	increase	increase	VERB
ijassa-497	243	16	the	the	DET
ijassa-497	243	17	score	score	NOUN
ijassa-497	243	18	on	on	ADP
ijassa-497	243	19	both	both	CCONJ
ijassa-497	243	20	low	low	ADJ
ijassa-497	243	21	fn	fn	NOUN
ijassa-497	243	22	and	and	CCONJ
ijassa-497	243	23	low	low	ADJ
ijassa-497	243	24	fp	fp	ADJ
ijassa-497	243	25	profiles	profile	NOUN
ijassa-497	243	26	,	,	PUNCT
ijassa-497	243	27	meanwhile	meanwhile	ADV
ijassa-497	243	28	it	it	PRON
ijassa-497	243	29	results	result	VERB
ijassa-497	243	30	in	in	ADP
ijassa-497	243	31	slight	slight	ADJ
ijassa-497	243	32	score	score	NOUN
ijassa-497	243	33	decrease	decrease	NOUN
ijassa-497	243	34	on	on	ADP
ijassa-497	243	35	standard	standard	ADJ
ijassa-497	243	36	profile	profile	NOUN
ijassa-497	243	37	.	.	PUNCT
ijassa-497	244	1	6	6	X
ijassa-497	244	2	.	.	X
ijassa-497	244	3	conclusion	conclusion	NOUN
ijassa-497	244	4	in	in	ADP
ijassa-497	244	5	this	this	DET
ijassa-497	244	6	paper	paper	NOUN
ijassa-497	244	7	we	we	PRON
ijassa-497	244	8	propose	propose	VERB
ijassa-497	244	9	an	an	DET
ijassa-497	244	10	algorithm	algorithm	NOUN
ijassa-497	244	11	for	for	ADP
ijassa-497	244	12	anomaly	anomaly	NOUN
ijassa-497	244	13	detection	detection	NOUN
ijassa-497	244	14	in	in	ADP
ijassa-497	244	15	time	time	NOUN
ijassa-497	244	16	series	series	PROPN
ijassa-497	244	17	data	data	PROPN
ijassa-497	244	18	,	,	PUNCT
ijassa-497	244	19	utilizing	utilize	VERB
ijassa-497	244	20	the	the	DET
ijassa-497	244	21	concept	concept	NOUN
ijassa-497	244	22	of	of	ADP
ijassa-497	244	23	expected	expect	VERB
ijassa-497	244	24	similarity	similarity	NOUN
ijassa-497	244	25	and	and	CCONJ
ijassa-497	244	26	applying	apply	VERB
ijassa-497	244	27	framework	framework	NOUN
ijassa-497	244	28	of	of	ADP
ijassa-497	244	29	conformal	conformal	ADJ
ijassa-497	244	30	anomaly	anomaly	NOUN
ijassa-497	244	31	detection	detection	NOUN
ijassa-497	244	32	.	.	PUNCT
ijassa-497	245	1	copyright	copyright	NOUN
ijassa-497	245	2	©	©	PROPN
ijassa-497	245	3	2017	2017	NUM
ijassa-497	245	4	assa	assa	NOUN
ijassa-497	245	5	.	.	PUNCT
ijassa-497	246	1	adv	adv	PROPN
ijassa-497	246	2	syst	syst	PROPN
ijassa-497	246	3	sci	sci	PROPN
ijassa-497	246	4	appl	appl	PROPN
ijassa-497	246	5	(	(	PUNCT
ijassa-497	246	6	2017	2017	NUM
ijassa-497	246	7	)	)	PUNCT
ijassa-497	246	8	conformal	conformal	ADJ
ijassa-497	246	9	kernel	kernel	NOUN
ijassa-497	246	10	expected	expect	VERB
ijassa-497	246	11	similarity	similarity	NOUN
ijassa-497	246	12	for	for	ADP
ijassa-497	246	13	anomaly	anomaly	NOUN
ijassa-497	246	14	detection	detection	NOUN
ijassa-497	246	15	in	in	ADP
ijassa-497	246	16	time	time	NOUN
ijassa-497	246	17	-	-	PUNCT
ijassa-497	246	18	series	series	NOUN
ijassa-497	246	19	data	datum	NOUN
ijassa-497	246	20	31	31	NUM
ijassa-497	246	21	table	table	NOUN
ijassa-497	246	22	5.2	5.2	NUM
ijassa-497	246	23	.	.	PUNCT
ijassa-497	247	1	results	result	NOUN
ijassa-497	247	2	on	on	ADP
ijassa-497	247	3	numenta	numenta	PROPN
ijassa-497	247	4	anomaly	anomaly	NOUN
ijassa-497	247	5	benchmark	benchmark	NOUN
ijassa-497	247	6	detector	detector	NOUN
ijassa-497	247	7	profile	profile	PROPN
ijassa-497	247	8	standard	standard	ADJ
ijassa-497	247	9	reward	reward	VERB
ijassa-497	247	10	low	low	ADJ
ijassa-497	247	11	fp	fp	PROPN
ijassa-497	247	12	reward	reward	NOUN
ijassa-497	247	13	low	low	PROPN
ijassa-497	247	14	fn	fn	PROPN
ijassa-497	247	15	numenta	numenta	PROPN
ijassa-497	247	16	htm	htm	PROPN
ijassa-497	247	17	70.1	70.1	NUM
ijassa-497	247	18	63.1	63.1	NUM
ijassa-497	247	19	74.3	74.3	NUM
ijassa-497	247	20	expose	expose	VERB
ijassa-497	247	21	ldcd	ldcd	NOUN
ijassa-497	247	22	+	+	ADP
ijassa-497	247	23	tuning	tune	VERB
ijassa-497	247	24	45.53	45.53	NUM
ijassa-497	247	25	25.77	25.77	NUM
ijassa-497	247	26	54.78	54.78	NUM
ijassa-497	247	27	windowed	windowed	ADJ
ijassa-497	247	28	gaussian	gaussian	NOUN
ijassa-497	247	29	39.6	39.6	NUM
ijassa-497	247	30	20.9	20.9	NUM
ijassa-497	247	31	47.4	47.4	NUM
ijassa-497	247	32	expose	expose	VERB
ijassa-497	247	33	ldcd	ldcd	PROPN
ijassa-497	247	34	37.93	37.93	NUM
ijassa-497	247	35	20.14	20.14	NUM
ijassa-497	247	36	45.11	45.11	NUM
ijassa-497	247	37	etsy	etsy	NOUN
ijassa-497	247	38	skyline	skyline	NOUN
ijassa-497	247	39	35.7	35.7	NUM
ijassa-497	247	40	27.1	27.1	NUM
ijassa-497	247	41	44.5	44.5	NUM
ijassa-497	247	42	bayesian	bayesian	NOUN
ijassa-497	247	43	changepoint	changepoint	VERB
ijassa-497	247	44	17.7	17.7	NUM
ijassa-497	247	45	3.2	3.2	NUM
ijassa-497	247	46	32.2	32.2	NUM
ijassa-497	247	47	expose	expose	VERB
ijassa-497	247	48	16.4	16.4	NUM
ijassa-497	247	49	3.2	3.2	NUM
ijassa-497	247	50	26.9	26.9	NUM
ijassa-497	247	51	table	table	NOUN
ijassa-497	247	52	5.3	5.3	NUM
ijassa-497	247	53	.	.	PUNCT
ijassa-497	248	1	results	result	NOUN
ijassa-497	248	2	on	on	ADP
ijassa-497	248	3	yahoo	yahoo	PROPN
ijassa-497	248	4	!	!	PUNCT
ijassa-497	248	5	s5	s5	PROPN
ijassa-497	248	6	dataset	dataset	VERB
ijassa-497	248	7	detector	detector	NOUN
ijassa-497	248	8	profile	profile	PROPN
ijassa-497	248	9	standard	standard	ADJ
ijassa-497	248	10	reward	reward	VERB
ijassa-497	248	11	low	low	ADJ
ijassa-497	248	12	fp	fp	PROPN
ijassa-497	248	13	reward	reward	NOUN
ijassa-497	248	14	low	low	ADV
ijassa-497	248	15	fn	fn	NOUN
ijassa-497	248	16	expose	expose	VERB
ijassa-497	248	17	ldcd	ldcd	NOUN
ijassa-497	248	18	51.88	51.88	NUM
ijassa-497	248	19	38.76	38.76	NUM
ijassa-497	248	20	58.95	58.95	NUM
ijassa-497	248	21	expose	expose	VERB
ijassa-497	248	22	ldcd	ldcd	NOUN
ijassa-497	248	23	+	+	ADP
ijassa-497	248	24	tuning	tune	VERB
ijassa-497	248	25	49.79	49.79	NUM
ijassa-497	248	26	43.73	43.73	NUM
ijassa-497	248	27	61.45	61.45	NUM
ijassa-497	248	28	numenta	numenta	PROPN
ijassa-497	248	29	htm	htm	PROPN
ijassa-497	248	30	41.0	41.0	NUM
ijassa-497	248	31	37.5	37.5	NUM
ijassa-497	248	32	44.4	44.4	NUM
ijassa-497	248	33	bayesian	bayesian	NOUN
ijassa-497	248	34	changepoint	changepoint	VERB
ijassa-497	248	35	35.7	35.7	NUM
ijassa-497	248	36	17.6	17.6	NUM
ijassa-497	248	37	43.6	43.6	NUM
ijassa-497	248	38	expose	expose	VERB
ijassa-497	248	39	32.09	32.09	NUM
ijassa-497	248	40	7.00	7.00	NUM
ijassa-497	248	41	45.45	45.45	NUM
ijassa-497	248	42	windowed	windowed	ADJ
ijassa-497	248	43	gaussian	gaussian	NOUN
ijassa-497	248	44	31.1	31.1	NUM
ijassa-497	248	45	25.8	25.8	NUM
ijassa-497	248	46	40.7	40.7	NUM
ijassa-497	248	47	etsy	etsy	NOUN
ijassa-497	248	48	skyline	skyline	NOUN
ijassa-497	248	49	23.6	23.6	NUM
ijassa-497	248	50	18.0	18.0	NUM
ijassa-497	248	51	28.9	28.9	NUM
ijassa-497	248	52	table	table	NOUN
ijassa-497	248	53	5.4	5.4	NUM
ijassa-497	248	54	.	.	PUNCT
ijassa-497	249	1	average	average	ADJ
ijassa-497	249	2	running	running	NOUN
ijassa-497	249	3	time	time	NOUN
ijassa-497	249	4	performance	performance	NOUN
ijassa-497	249	5	on	on	ADP
ijassa-497	249	6	nab	nab	ADJ
ijassa-497	249	7	dataset	dataset	NOUN
ijassa-497	249	8	detector	detector	NOUN
ijassa-497	249	9	performance	performance	NOUN
ijassa-497	249	10	items	item	NOUN
ijassa-497	249	11	per	per	ADP
ijassa-497	249	12	second	second	ADJ
ijassa-497	249	13	ms	ms	NOUN
ijassa-497	249	14	per	per	ADP
ijassa-497	249	15	item	item	NOUN
ijassa-497	249	16	windowed	windowe	VERB
ijassa-497	249	17	gaussian	gaussian	ADJ
ijassa-497	249	18	1984.862	1984.862	NUM
ijassa-497	249	19	0.504	0.504	NUM
ijassa-497	249	20	expose	expose	VERB
ijassa-497	249	21	ldcd	ldcd	PROPN
ijassa-497	249	22	1500.224	1500.224	NUM
ijassa-497	249	23	0.667	0.667	NUM
ijassa-497	249	24	bayesian	bayesian	NOUN
ijassa-497	249	25	changepoint	changepoint	VERB
ijassa-497	249	26	428.639	428.639	NUM
ijassa-497	249	27	2.333	2.333	NUM
ijassa-497	249	28	expose	expose	VERB
ijassa-497	249	29	398.496	398.496	NUM
ijassa-497	249	30	2.51	2.51	NUM
ijassa-497	249	31	numenta	numenta	PROPN
ijassa-497	249	32	htm	htm	PROPN
ijassa-497	249	33	98.012	98.012	NUM
ijassa-497	249	34	10.202	10.202	NUM
ijassa-497	249	35	etsy	etsy	NOUN
ijassa-497	249	36	skyline	skyline	VERB
ijassa-497	249	37	4.582	4.582	NUM
ijassa-497	249	38	218.229	218.229	NUM
ijassa-497	249	39	table	table	NOUN
ijassa-497	249	40	5.5	5.5	NUM
ijassa-497	249	41	.	.	PUNCT
ijassa-497	250	1	average	average	ADJ
ijassa-497	250	2	running	running	NOUN
ijassa-497	250	3	time	time	NOUN
ijassa-497	250	4	performance	performance	NOUN
ijassa-497	250	5	on	on	ADP
ijassa-497	250	6	yahoo	yahoo	PROPN
ijassa-497	250	7	!	!	PUNCT
ijassa-497	250	8	s5	s5	PROPN
ijassa-497	250	9	dataset	dataset	VERB
ijassa-497	250	10	detector	detector	NOUN
ijassa-497	250	11	performance	performance	NOUN
ijassa-497	250	12	items	item	NOUN
ijassa-497	250	13	per	per	ADP
ijassa-497	250	14	second	second	ADJ
ijassa-497	250	15	ms	ms	NOUN
ijassa-497	250	16	per	per	ADP
ijassa-497	250	17	item	item	NOUN
ijassa-497	250	18	expose	expose	VERB
ijassa-497	250	19	ldcd	ldcd	NOUN
ijassa-497	250	20	2548.293	2548.293	NUM
ijassa-497	250	21	0.392	0.392	NUM
ijassa-497	250	22	windowed	windowed	ADJ
ijassa-497	250	23	gaussian	gaussian	ADJ
ijassa-497	250	24	2348.041	2348.041	NUM
ijassa-497	250	25	0.426	0.426	NUM
ijassa-497	250	26	bayesian	bayesian	NOUN
ijassa-497	250	27	changepoint	changepoint	VERB
ijassa-497	250	28	1217.888	1217.888	NUM
ijassa-497	250	29	0.821	0.821	NUM
ijassa-497	250	30	expose	expose	VERB
ijassa-497	250	31	383.711	383.711	NUM
ijassa-497	250	32	2.606	2.606	NUM
ijassa-497	250	33	numenta	numenta	NOUN
ijassa-497	250	34	htm	htm	PROPN
ijassa-497	250	35	103.777	103.777	NUM
ijassa-497	250	36	9.636	9.636	NUM
ijassa-497	250	37	etsy	etsy	NOUN
ijassa-497	250	38	skyline	skyline	NOUN
ijassa-497	250	39	4.656	4.656	NUM
ijassa-497	250	40	214.773	214.773	PROPN
ijassa-497	250	41	this	this	DET
ijassa-497	250	42	approach	approach	NOUN
ijassa-497	250	43	has	have	AUX
ijassa-497	250	44	been	be	AUX
ijassa-497	250	45	rigorously	rigorously	ADV
ijassa-497	250	46	validated	validate	VERB
ijassa-497	250	47	on	on	ADP
ijassa-497	250	48	nab	nab	NOUN
ijassa-497	250	49	corpus	corpus	NOUN
ijassa-497	250	50	and	and	CCONJ
ijassa-497	250	51	yahoo	yahoo	PROPN
ijassa-497	250	52	!	!	PUNCT
ijassa-497	250	53	s5	s5	PROPN
ijassa-497	250	54	dataset	dataset	VERB
ijassa-497	250	55	using	use	VERB
ijassa-497	250	56	numenta	numenta	PROPN
ijassa-497	250	57	anomaly	anomaly	NOUN
ijassa-497	250	58	benchmark	benchmark	NOUN
ijassa-497	250	59	.	.	PUNCT
ijassa-497	251	1	on	on	ADP
ijassa-497	251	2	both	both	DET
ijassa-497	251	3	datasets	dataset	NOUN
ijassa-497	251	4	the	the	DET
ijassa-497	251	5	proposed	propose	VERB
ijassa-497	251	6	approach	approach	NOUN
ijassa-497	251	7	excel	excel	VERB
ijassa-497	251	8	the	the	DET
ijassa-497	251	9	expose	expose	NOUN
ijassa-497	251	10	,	,	PUNCT
ijassa-497	251	11	which	which	PRON
ijassa-497	251	12	produces	produce	VERB
ijassa-497	251	13	expected	expect	VERB
ijassa-497	251	14	similarity	similarity	NOUN
ijassa-497	251	15	as	as	ADP
ijassa-497	251	16	anomaly	anomaly	NOUN
ijassa-497	251	17	score	score	NOUN
ijassa-497	251	18	and	and	CCONJ
ijassa-497	251	19	expected	expect	VERB
ijassa-497	251	20	similarity	similarity	NOUN
ijassa-497	251	21	is	be	AUX
ijassa-497	251	22	used	use	VERB
ijassa-497	251	23	as	as	ADP
ijassa-497	251	24	nonconformity	nonconformity	NOUN
ijassa-497	251	25	measure	measure	NOUN
ijassa-497	251	26	in	in	ADP
ijassa-497	251	27	the	the	DET
ijassa-497	251	28	ldcd	ldcd	ADJ
ijassa-497	251	29	procedure	procedure	NOUN
ijassa-497	251	30	.	.	PUNCT
ijassa-497	252	1	moreover	moreover	ADV
ijassa-497	252	2	,	,	PUNCT
ijassa-497	252	3	the	the	DET
ijassa-497	252	4	developed	develop	VERB
ijassa-497	252	5	algorithm	algorithm	NOUN
ijassa-497	252	6	shows	show	VERB
ijassa-497	252	7	great	great	ADJ
ijassa-497	252	8	running	running	NOUN
ijassa-497	252	9	time	time	NOUN
ijassa-497	252	10	performance	performance	NOUN
ijassa-497	252	11	,	,	PUNCT
ijassa-497	252	12	which	which	PRON
ijassa-497	252	13	is	be	AUX
ijassa-497	252	14	important	important	ADJ
ijassa-497	252	15	for	for	ADP
ijassa-497	252	16	online	online	ADJ
ijassa-497	252	17	detectors	detector	NOUN
ijassa-497	252	18	and	and	CCONJ
ijassa-497	252	19	achieves	achieve	VERB
ijassa-497	252	20	high	high	ADJ
ijassa-497	252	21	results	result	NOUN
ijassa-497	252	22	on	on	ADP
ijassa-497	252	23	standard	standard	ADJ
ijassa-497	252	24	profile	profile	NOUN
ijassa-497	252	25	on	on	ADP
ijassa-497	252	26	yahoo	yahoo	PROPN
ijassa-497	252	27	dataset	dataset	NOUN
ijassa-497	252	28	.	.	PUNCT
ijassa-497	253	1	also	also	ADV
ijassa-497	253	2	,	,	PUNCT
ijassa-497	253	3	the	the	DET
ijassa-497	253	4	implementation	implementation	NOUN
ijassa-497	253	5	of	of	ADP
ijassa-497	253	6	the	the	DET
ijassa-497	253	7	algorithm	algorithm	NOUN
ijassa-497	253	8	could	could	AUX
ijassa-497	253	9	be	be	AUX
ijassa-497	253	10	enhanced	enhance	VERB
ijassa-497	253	11	,	,	PUNCT
ijassa-497	253	12	as	as	SCONJ
ijassa-497	253	13	it	it	PRON
ijassa-497	253	14	has	have	AUX
ijassa-497	253	15	not	not	PART
ijassa-497	253	16	been	be	AUX
ijassa-497	253	17	thoroughly	thoroughly	ADV
ijassa-497	253	18	optimised	optimise	VERB
ijassa-497	253	19	and	and	CCONJ
ijassa-497	253	20	it	it	PRON
ijassa-497	253	21	could	could	AUX
ijassa-497	253	22	be	be	AUX
ijassa-497	253	23	one	one	NUM
ijassa-497	253	24	of	of	ADP
ijassa-497	253	25	the	the	DET
ijassa-497	253	26	directions	direction	NOUN
ijassa-497	253	27	for	for	ADP
ijassa-497	253	28	future	future	ADJ
ijassa-497	253	29	research	research	NOUN
ijassa-497	253	30	.	.	PUNCT
ijassa-497	254	1	we	we	PRON
ijassa-497	254	2	also	also	ADV
ijassa-497	254	3	propose	propose	VERB
ijassa-497	254	4	a	a	DET
ijassa-497	254	5	tuning	tuning	NOUN
ijassa-497	254	6	procedure	procedure	NOUN
ijassa-497	254	7	for	for	ADP
ijassa-497	254	8	the	the	DET
ijassa-497	254	9	kernel	kernel	PROPN
ijassa-497	254	10	bandwidth	bandwidth	PROPN
ijassa-497	254	11	parameter	parameter	NOUN
ijassa-497	254	12	,	,	PUNCT
ijassa-497	254	13	however	however	ADV
ijassa-497	254	14	there	there	PRON
ijassa-497	254	15	is	be	VERB
ijassa-497	254	16	still	still	ADV
ijassa-497	254	17	a	a	DET
ijassa-497	254	18	significant	significant	ADJ
ijassa-497	254	19	room	room	NOUN
ijassa-497	254	20	for	for	ADP
ijassa-497	254	21	improvements	improvement	NOUN
ijassa-497	254	22	.	.	PUNCT
ijassa-497	255	1	acknowledgements	acknowledgement	NOUN
ijassa-497	255	2	the	the	DET
ijassa-497	255	3	work	work	NOUN
ijassa-497	255	4	was	be	AUX
ijassa-497	255	5	supported	support	VERB
ijassa-497	255	6	by	by	ADP
ijassa-497	255	7	the	the	DET
ijassa-497	255	8	ministry	ministry	PROPN
ijassa-497	255	9	of	of	ADP
ijassa-497	255	10	education	education	PROPN
ijassa-497	255	11	and	and	CCONJ
ijassa-497	255	12	science	science	NOUN
ijassa-497	255	13	of	of	ADP
ijassa-497	255	14	russian	russian	PROPN
ijassa-497	255	15	federation	federation	PROPN
ijassa-497	255	16	,	,	PUNCT
ijassa-497	255	17	grant	grant	VERB
ijassa-497	255	18	no	no	INTJ
ijassa-497	255	19	.	.	PUNCT
ijassa-497	256	1	14.606.21.0004	14.606.21.0004	NUM
ijassa-497	256	2	,	,	PUNCT
ijassa-497	256	3	grant	grant	PROPN
ijassa-497	256	4	code	code	NOUN
ijassa-497	256	5	:	:	PUNCT
ijassa-497	256	6	rfmefi60617x0004	rfmefi60617x0004	PROPN
ijassa-497	256	7	.	.	PUNCT
ijassa-497	257	1	copyright	copyright	NOUN
ijassa-497	257	2	©	©	PROPN
ijassa-497	257	3	2017	2017	NUM
ijassa-497	257	4	assa	assa	NOUN
ijassa-497	257	5	.	.	PUNCT
ijassa-497	258	1	adv	adv	PROPN
ijassa-497	258	2	syst	syst	PROPN
ijassa-497	258	3	sci	sci	PROPN
ijassa-497	258	4	appl	appl	PROPN
ijassa-497	258	5	(	(	PUNCT
ijassa-497	258	6	2017	2017	NUM
ijassa-497	258	7	)	)	PUNCT
ijassa-497	258	8	32	32	NUM
ijassa-497	258	9	aleksandr	aleksandr	PROPN
ijassa-497	258	10	safin	safin	PROPN
ijassa-497	258	11	,	,	PUNCT
ijassa-497	258	12	evgeny	evgeny	PROPN
ijassa-497	258	13	burnaev	burnaev	PROPN
ijassa-497	258	14	references	reference	NOUN
ijassa-497	258	15	1	1	NUM
ijassa-497	258	16	.	.	PUNCT
ijassa-497	258	17	alestra	alestra	PROPN
ijassa-497	258	18	s.	s.	PROPN
ijassa-497	258	19	,	,	PUNCT
ijassa-497	258	20	bordry	bordry	PROPN
ijassa-497	258	21	c.	c.	PROPN
ijassa-497	258	22	,	,	PUNCT
ijassa-497	258	23	brand	brand	PROPN
ijassa-497	258	24	c.	c.	PROPN
ijassa-497	258	25	,	,	PUNCT
ijassa-497	258	26	burnaev	burnaev	PROPN
ijassa-497	258	27	e.	e.	PROPN
ijassa-497	258	28	,	,	PUNCT
ijassa-497	258	29	erofeev	erofeev	PROPN
ijassa-497	258	30	p.	p.	NOUN
ijassa-497	258	31	,	,	PUNCT
ijassa-497	258	32	papanov	papanov	PROPN
ijassa-497	258	33	a.	a.	PROPN
ijassa-497	258	34	&	&	CCONJ
ijassa-497	258	35	silveirafreixo	silveirafreixo	PROPN
ijassa-497	258	36	c.	c.	PROPN
ijassa-497	258	37	(	(	PUNCT
ijassa-497	258	38	2014	2014	NUM
ijassa-497	258	39	)	)	PUNCT
ijassa-497	258	40	application	application	NOUN
ijassa-497	258	41	of	of	ADP
ijassa-497	258	42	rare	rare	ADJ
ijassa-497	258	43	event	event	NOUN
ijassa-497	258	44	anticipation	anticipation	NOUN
ijassa-497	258	45	techniques	technique	NOUN
ijassa-497	258	46	to	to	ADP
ijassa-497	258	47	aircraft	aircraft	NOUN
ijassa-497	258	48	health	health	NOUN
ijassa-497	258	49	management	management	NOUN
ijassa-497	258	50	advanced	advance	VERB
ijassa-497	258	51	materials	material	NOUN
ijassa-497	258	52	research	research	NOUN
ijassa-497	258	53	,	,	PUNCT
ijassa-497	258	54	1016	1016	NUM
ijassa-497	258	55	,	,	PUNCT
ijassa-497	258	56	413–417	413–417	NUM
ijassa-497	258	57	.	.	NOUN
ijassa-497	259	1	2	2	NUM
ijassa-497	259	2	.	.	PUNCT
ijassa-497	259	3	burnaev	burnaev	PROPN
ijassa-497	259	4	e.	e.	PROPN
ijassa-497	259	5	,	,	PUNCT
ijassa-497	259	6	erofeev	erofeev	PROPN
ijassa-497	259	7	p.	p.	PROPN
ijassa-497	259	8	&	&	CCONJ
ijassa-497	259	9	smolyakov	smolyakov	PROPN
ijassa-497	259	10	d.	d.	PROPN
ijassa-497	259	11	(	(	PUNCT
ijassa-497	259	12	2015	2015	NUM
ijassa-497	259	13	)	)	PUNCT
ijassa-497	259	14	model	model	NOUN
ijassa-497	259	15	selection	selection	NOUN
ijassa-497	259	16	for	for	ADP
ijassa-497	259	17	anomaly	anomaly	NOUN
ijassa-497	259	18	detection	detection	NOUN
ijassa-497	259	19	.	.	PUNCT
ijassa-497	260	1	proc	proc	NOUN
ijassa-497	260	2	.	.	PUNCT
ijassa-497	261	1	spie9875	spie9875	PROPN
ijassa-497	261	2	,	,	PUNCT
ijassa-497	261	3	eighth	eighth	ADJ
ijassa-497	261	4	international	international	ADJ
ijassa-497	261	5	conference	conference	NOUN
ijassa-497	261	6	on	on	ADP
ijassa-497	261	7	machine	machine	NOUN
ijassa-497	261	8	vision	vision	NOUN
ijassa-497	261	9	(	(	PUNCT
ijassa-497	261	10	icmv	icmv	VERB
ijassa-497	261	11	2015	2015	NUM
ijassa-497	261	12	)	)	PUNCT
ijassa-497	261	13	,	,	PUNCT
ijassa-497	261	14	987525	987525	NUM
ijassa-497	261	15	,	,	PUNCT
ijassa-497	261	16	http://dx.doi.org/10.1117/12.2228794	http://dx.doi.org/10.1117/12.2228794	PROPN
ijassa-497	261	17	3	3	NUM
ijassa-497	261	18	.	.	PUNCT
ijassa-497	261	19	artemov	artemov	PROPN
ijassa-497	261	20	a.	a.	PROPN
ijassa-497	261	21	&	&	CCONJ
ijassa-497	261	22	burnaev	burnaev	PROPN
ijassa-497	261	23	e.	e.	PROPN
ijassa-497	261	24	(	(	PUNCT
ijassa-497	261	25	2015	2015	NUM
ijassa-497	261	26	)	)	PUNCT
ijassa-497	261	27	ensembles	ensemble	NOUN
ijassa-497	261	28	of	of	ADP
ijassa-497	261	29	detectors	detector	NOUN
ijassa-497	261	30	for	for	ADP
ijassa-497	261	31	online	online	ADJ
ijassa-497	261	32	detection	detection	NOUN
ijassa-497	261	33	of	of	ADP
ijassa-497	261	34	transient	transient	ADJ
ijassa-497	261	35	changes	change	NOUN
ijassa-497	261	36	.	.	PUNCT
ijassa-497	262	1	proc	proc	NOUN
ijassa-497	262	2	.	.	PUNCT
ijassa-497	263	1	spie9875	spie9875	PROPN
ijassa-497	263	2	,	,	PUNCT
ijassa-497	263	3	eighth	eighth	ADJ
ijassa-497	263	4	international	international	ADJ
ijassa-497	263	5	conference	conference	NOUN
ijassa-497	263	6	on	on	ADP
ijassa-497	263	7	machine	machine	NOUN
ijassa-497	263	8	vision	vision	NOUN
ijassa-497	263	9	(	(	PUNCT
ijassa-497	263	10	icmv	icmv	VERB
ijassa-497	263	11	2015	2015	NUM
ijassa-497	263	12	)	)	PUNCT
ijassa-497	263	13	,	,	PUNCT
ijassa-497	263	14	98751z	98751z	NUM
ijassa-497	263	15	,	,	PUNCT
ijassa-497	263	16	http://dx.doi.org/10.1117/12.2228369	http://dx.doi.org/10.1117/12.2228369	PROPN
ijassa-497	263	17	4	4	NUM
ijassa-497	263	18	.	.	PUNCT
ijassa-497	263	19	burnaev	burnaev	PROPN
ijassa-497	263	20	e.	e.	PROPN
ijassa-497	263	21	,	,	PUNCT
ijassa-497	263	22	erofeev	erofeev	PROPN
ijassa-497	263	23	p.	p.	PROPN
ijassa-497	263	24	&	&	CCONJ
ijassa-497	263	25	papanov	papanov	PROPN
ijassa-497	263	26	a.	a.	PROPN
ijassa-497	263	27	(	(	PUNCT
ijassa-497	263	28	2015	2015	NUM
ijassa-497	263	29	)	)	PUNCT
ijassa-497	263	30	influence	influence	NOUN
ijassa-497	263	31	of	of	ADP
ijassa-497	263	32	resampling	resample	VERB
ijassa-497	263	33	on	on	ADP
ijassa-497	263	34	accuracy	accuracy	NOUN
ijassa-497	263	35	of	of	ADP
ijassa-497	263	36	imbalanced	imbalanced	ADJ
ijassa-497	263	37	classification	classification	NOUN
ijassa-497	263	38	.	.	PUNCT
ijassa-497	264	1	proc	proc	NOUN
ijassa-497	264	2	.	.	PUNCT
ijassa-497	265	1	spie9875	spie9875	PROPN
ijassa-497	265	2	,	,	PUNCT
ijassa-497	265	3	eighth	eighth	ADJ
ijassa-497	265	4	international	international	ADJ
ijassa-497	265	5	conference	conference	NOUN
ijassa-497	265	6	on	on	ADP
ijassa-497	265	7	machine	machine	NOUN
ijassa-497	265	8	vision	vision	NOUN
ijassa-497	265	9	(	(	PUNCT
ijassa-497	265	10	icmv	icmv	VERB
ijassa-497	265	11	2015	2015	NUM
ijassa-497	265	12	)	)	PUNCT
ijassa-497	265	13	,	,	PUNCT
ijassa-497	265	14	987521	987521	NUM
ijassa-497	265	15	,	,	PUNCT
ijassa-497	265	16	http://dx.doi.org/10.1117/12.2228523	http://dx.doi.org/10.1117/12.2228523	NUM
ijassa-497	265	17	5	5	NUM
ijassa-497	265	18	.	.	PUNCT
ijassa-497	265	19	burnaev	burnaev	PROPN
ijassa-497	265	20	e.	e.	PROPN
ijassa-497	265	21	,	,	PUNCT
ijassa-497	265	22	erofeev	erofeev	PROPN
ijassa-497	265	23	p.	p.	PROPN
ijassa-497	265	24	&	&	CCONJ
ijassa-497	265	25	papanov	papanov	PROPN
ijassa-497	265	26	a.	a.	PROPN
ijassa-497	265	27	(	(	PUNCT
ijassa-497	265	28	2017	2017	NUM
ijassa-497	265	29	)	)	PUNCT
ijassa-497	265	30	meta	meta	NOUN
ijassa-497	265	31	-	-	PUNCT
ijassa-497	265	32	learning	learning	NOUN
ijassa-497	265	33	for	for	ADP
ijassa-497	265	34	construction	construction	NOUN
ijassa-497	265	35	of	of	ADP
ijassa-497	265	36	resampling	resample	VERB
ijassa-497	265	37	recommendation	recommendation	NOUN
ijassa-497	265	38	systems	system	NOUN
ijassa-497	265	39	.	.	PUNCT
ijassa-497	266	1	arxiv	arxiv	PROPN
ijassa-497	266	2	e	e	PROPN
ijassa-497	266	3	-	-	NOUN
ijassa-497	266	4	prints	print	NOUN
ijassa-497	266	5	,	,	PUNCT
ijassa-497	266	6	1706.02289	1706.02289	ADJ
ijassa-497	266	7	,	,	PUNCT
ijassa-497	266	8	[	[	X
ijassa-497	266	9	online	online	X
ijassa-497	266	10	]	]	X
ijassa-497	266	11	.	.	PUNCT
ijassa-497	267	1	available	available	ADJ
ijassa-497	267	2	:	:	PUNCT
ijassa-497	267	3	https://arxiv.org	https://arxiv.org	NUM
ijassa-497	267	4	/	/	SYM
ijassa-497	267	5	abs/1706.02289	abs/1706.02289	NOUN
ijassa-497	267	6	6	6	NUM
ijassa-497	267	7	.	.	PUNCT
ijassa-497	267	8	burnaev	burnaev	PROPN
ijassa-497	267	9	e	e	PROPN
ijassa-497	267	10	&	&	CCONJ
ijassa-497	267	11	smolyakov	smolyakov	PROPN
ijassa-497	267	12	d.	d.	PROPN
ijassa-497	267	13	(	(	PUNCT
ijassa-497	267	14	2016	2016	NUM
ijassa-497	267	15	)	)	PUNCT
ijassa-497	267	16	one	one	NUM
ijassa-497	267	17	-	-	PUNCT
ijassa-497	267	18	class	class	NOUN
ijassa-497	267	19	svm	svm	NOUN
ijassa-497	267	20	with	with	ADP
ijassa-497	267	21	privileged	privileged	ADJ
ijassa-497	267	22	information	information	NOUN
ijassa-497	267	23	and	and	CCONJ
ijassa-497	267	24	its	its	PRON
ijassa-497	267	25	application	application	NOUN
ijassa-497	267	26	to	to	ADP
ijassa-497	267	27	malware	malware	NOUN
ijassa-497	267	28	detection	detection	NOUN
ijassa-497	267	29	.	.	PUNCT
ijassa-497	268	1	2016	2016	NUM
ijassa-497	268	2	ieee	ieee	NOUN
ijassa-497	268	3	16th	16th	ADJ
ijassa-497	268	4	international	international	ADJ
ijassa-497	268	5	conference	conference	NOUN
ijassa-497	268	6	on	on	ADP
ijassa-497	268	7	data	datum	NOUN
ijassa-497	268	8	mining	mining	NOUN
ijassa-497	268	9	workshops	workshop	NOUN
ijassa-497	268	10	(	(	PUNCT
ijassa-497	268	11	icdmw	icdmw	PROPN
ijassa-497	268	12	)	)	PUNCT
ijassa-497	268	13	,	,	PUNCT
ijassa-497	268	14	pp	pp	ADJ
ijassa-497	268	15	.	.	PUNCT
ijassa-497	269	1	273–280	273–280	NUM
ijassa-497	269	2	.	.	NOUN
ijassa-497	269	3	7	7	X
ijassa-497	269	4	.	.	PUNCT
ijassa-497	269	5	burnaev	burnaev	PROPN
ijassa-497	269	6	e.	e.	PROPN
ijassa-497	269	7	,	,	PUNCT
ijassa-497	269	8	ishimtsev	ishimtsev	PROPN
ijassa-497	269	9	v.	v.	PROPN
ijassa-497	269	10	,	,	PUNCT
ijassa-497	269	11	bernstein	bernstein	PROPN
ijassa-497	269	12	a.	a.	PROPN
ijassa-497	269	13	&	&	CCONJ
ijassa-497	269	14	nazarov	nazarov	PROPN
ijassa-497	269	15	a.	a.	NOUN
ijassa-497	269	16	(	(	PUNCT
ijassa-497	269	17	2017	2017	NUM
ijassa-497	269	18	)	)	PUNCT
ijassa-497	269	19	conformal	conformal	NOUN
ijassa-497	269	20	k	k	PROPN
ijassa-497	269	21	-	-	PUNCT
ijassa-497	269	22	nn	nn	PROPN
ijassa-497	269	23	anomaly	anomaly	NOUN
ijassa-497	269	24	detector	detector	NOUN
ijassa-497	269	25	for	for	ADP
ijassa-497	269	26	univariate	univariate	ADJ
ijassa-497	269	27	data	datum	NOUN
ijassa-497	269	28	streams	stream	NOUN
ijassa-497	269	29	.	.	PUNCT
ijassa-497	270	1	proceedings	proceeding	NOUN
ijassa-497	270	2	of	of	ADP
ijassa-497	270	3	machine	machine	NOUN
ijassa-497	270	4	learning	learn	VERB
ijassa-497	270	5	research	research	NOUN
ijassa-497	270	6	,	,	PUNCT
ijassa-497	270	7	60	60	NUM
ijassa-497	270	8	,	,	PUNCT
ijassa-497	270	9	213–227	213–227	NUM
ijassa-497	270	10	.	.	PUNCT
ijassa-497	271	1	8	8	NUM
ijassa-497	271	2	.	.	PUNCT
ijassa-497	271	3	volkhonsky	volkhonsky	PROPN
ijassa-497	271	4	d.	d.	PROPN
ijassa-497	271	5	,	,	PUNCT
ijassa-497	271	6	burnaev	burnaev	PROPN
ijassa-497	271	7	e.	e.	PROPN
ijassa-497	271	8	,	,	PUNCT
ijassa-497	271	9	nouretdinov	nouretdinov	PROPN
ijassa-497	271	10	i.	i.	PROPN
ijassa-497	271	11	,	,	PUNCT
ijassa-497	271	12	gammerman	gammerman	NOUN
ijassa-497	271	13	a.	a.	PROPN
ijassa-497	271	14	&	&	CCONJ
ijassa-497	271	15	vovk	vovk	PROPN
ijassa-497	271	16	v.	v.	PROPN
ijassa-497	271	17	(	(	PUNCT
ijassa-497	271	18	2017	2017	NUM
ijassa-497	271	19	)	)	PUNCT
ijassa-497	271	20	inductive	inductive	VERB
ijassa-497	271	21	conformal	conformal	ADJ
ijassa-497	271	22	martingales	martingale	NOUN
ijassa-497	271	23	for	for	ADP
ijassa-497	271	24	change	change	NOUN
ijassa-497	271	25	-	-	PUNCT
ijassa-497	271	26	point	point	NOUN
ijassa-497	271	27	detection	detection	NOUN
ijassa-497	271	28	.	.	PUNCT
ijassa-497	272	1	proceedings	proceeding	NOUN
ijassa-497	272	2	of	of	ADP
ijassa-497	272	3	machine	machine	NOUN
ijassa-497	272	4	learning	learn	VERB
ijassa-497	272	5	research	research	NOUN
ijassa-497	272	6	,	,	PUNCT
ijassa-497	272	7	60	60	NUM
ijassa-497	272	8	,	,	PUNCT
ijassa-497	272	9	132–153	132–153	NUM
ijassa-497	272	10	.	.	PUNCT
ijassa-497	273	1	9	9	X
ijassa-497	273	2	.	.	X
ijassa-497	273	3	artemov	artemov	PROPN
ijassa-497	273	4	a.	a.	PROPN
ijassa-497	273	5	&	&	CCONJ
ijassa-497	273	6	burnaev	burnaev	PROPN
ijassa-497	273	7	e.	e.	PROPN
ijassa-497	273	8	(	(	PUNCT
ijassa-497	273	9	2016	2016	NUM
ijassa-497	273	10	)	)	PUNCT
ijassa-497	273	11	optimal	optimal	ADJ
ijassa-497	273	12	sequential	sequential	ADJ
ijassa-497	273	13	estimation	estimation	NOUN
ijassa-497	273	14	of	of	ADP
ijassa-497	273	15	a	a	DET
ijassa-497	273	16	signal	signal	NOUN
ijassa-497	273	17	,	,	PUNCT
ijassa-497	273	18	observed	observe	VERB
ijassa-497	273	19	in	in	ADP
ijassa-497	273	20	a	a	DET
ijassa-497	273	21	fractional	fractional	ADJ
ijassa-497	273	22	gaussian	gaussian	NOUN
ijassa-497	273	23	noise	noise	NOUN
ijassa-497	273	24	.	.	PUNCT
ijassa-497	274	1	theory	theory	NOUN
ijassa-497	274	2	of	of	ADP
ijassa-497	274	3	probability	probability	NOUN
ijassa-497	274	4	and	and	CCONJ
ijassa-497	274	5	its	its	PRON
ijassa-497	274	6	applications	application	NOUN
ijassa-497	274	7	,	,	PUNCT
ijassa-497	274	8	60(1	60(1	NUM
ijassa-497	274	9	)	)	PUNCT
ijassa-497	274	10	,	,	PUNCT
ijassa-497	274	11	126–134	126–134	NUM
ijassa-497	274	12	.	.	PUNCT
ijassa-497	274	13	10	10	NUM
ijassa-497	274	14	.	.	X
ijassa-497	274	15	artemov	artemov	PROPN
ijassa-497	274	16	a.	a.	PROPN
ijassa-497	274	17	&	&	CCONJ
ijassa-497	274	18	burnaev	burnaev	PROPN
ijassa-497	274	19	e.	e.	PROPN
ijassa-497	274	20	2016	2016	NUM
ijassa-497	274	21	)	)	PUNCT
ijassa-497	274	22	detecting	detect	VERB
ijassa-497	274	23	performance	performance	NOUN
ijassa-497	274	24	degradation	degradation	NOUN
ijassa-497	274	25	of	of	ADP
ijassa-497	274	26	softwareintensive	softwareintensive	ADJ
ijassa-497	274	27	systems	system	NOUN
ijassa-497	274	28	in	in	ADP
ijassa-497	274	29	the	the	DET
ijassa-497	274	30	presence	presence	NOUN
ijassa-497	274	31	of	of	ADP
ijassa-497	274	32	trends	trend	NOUN
ijassa-497	274	33	and	and	CCONJ
ijassa-497	274	34	long	long	ADJ
ijassa-497	274	35	-	-	PUNCT
ijassa-497	274	36	range	range	NOUN
ijassa-497	274	37	dependence	dependence	NOUN
ijassa-497	274	38	.	.	PUNCT
ijassa-497	275	1	2016	2016	NUM
ijassa-497	275	2	ieee	ieee	NOUN
ijassa-497	275	3	16th	16th	ADJ
ijassa-497	275	4	international	international	ADJ
ijassa-497	275	5	conference	conference	NOUN
ijassa-497	275	6	on	on	ADP
ijassa-497	275	7	data	datum	NOUN
ijassa-497	275	8	mining	mining	NOUN
ijassa-497	275	9	workshops	workshop	NOUN
ijassa-497	275	10	(	(	PUNCT
ijassa-497	275	11	icdmw	icdmw	PROPN
ijassa-497	275	12	)	)	PUNCT
ijassa-497	275	13	,	,	PUNCT
ijassa-497	275	14	pp	pp	ADJ
ijassa-497	275	15	.	.	PUNCT
ijassa-497	276	1	29–36	29–36	NUM
ijassa-497	276	2	.	.	PUNCT
ijassa-497	277	1	11	11	NUM
ijassa-497	277	2	.	.	X
ijassa-497	277	3	artemov	artemov	PROPN
ijassa-497	277	4	a.	a.	PROPN
ijassa-497	277	5	,	,	PUNCT
ijassa-497	277	6	burnaev	burnaev	PROPN
ijassa-497	277	7	e.	e.	PROPN
ijassa-497	277	8	&	&	CCONJ
ijassa-497	277	9	lokot	lokot	PROPN
ijassa-497	277	10	a.	a.	PROPN
ijassa-497	277	11	(	(	PUNCT
ijassa-497	277	12	2015	2015	NUM
ijassa-497	277	13	)	)	PUNCT
ijassa-497	277	14	nonparametric	nonparametric	NOUN
ijassa-497	277	15	decomposition	decomposition	NOUN
ijassa-497	277	16	of	of	ADP
ijassa-497	277	17	quasiperiodic	quasiperiodic	ADJ
ijassa-497	277	18	time	time	NOUN
ijassa-497	277	19	series	series	NOUN
ijassa-497	277	20	for	for	ADP
ijassa-497	277	21	change	change	NOUN
ijassa-497	277	22	-	-	PUNCT
ijassa-497	277	23	point	point	NOUN
ijassa-497	277	24	detection	detection	NOUN
ijassa-497	277	25	.	.	PUNCT
ijassa-497	278	1	proc	proc	NOUN
ijassa-497	278	2	.	.	PUNCT
ijassa-497	279	1	spie	spie	NOUN
ijassa-497	279	2	9875	9875	NUM
ijassa-497	279	3	,	,	PUNCT
ijassa-497	279	4	eighth	eighth	ADJ
ijassa-497	279	5	international	international	ADJ
ijassa-497	279	6	conference	conference	NOUN
ijassa-497	279	7	on	on	ADP
ijassa-497	279	8	machine	machine	NOUN
ijassa-497	279	9	vision	vision	NOUN
ijassa-497	279	10	,	,	PUNCT
ijassa-497	279	11	987520	987520	NUM
ijassa-497	279	12	.	.	PUNCT
ijassa-497	280	1	12	12	NUM
ijassa-497	280	2	.	.	PUNCT
ijassa-497	281	1	burnaev	burnaev	PROPN
ijassa-497	281	2	e.	e.	PROPN
ijassa-497	281	3	(	(	PUNCT
ijassa-497	281	4	2009	2009	NUM
ijassa-497	281	5	)	)	PUNCT
ijassa-497	281	6	disorder	disorder	NOUN
ijassa-497	281	7	problem	problem	NOUN
ijassa-497	281	8	for	for	ADP
ijassa-497	281	9	poisson	poisson	NOUN
ijassa-497	281	10	process	process	NOUN
ijassa-497	281	11	in	in	ADP
ijassa-497	281	12	generalized	generalized	ADJ
ijassa-497	281	13	bayesian	bayesian	NOUN
ijassa-497	281	14	setting	setting	NOUN
ijassa-497	281	15	.	.	PUNCT
ijassa-497	282	1	theory	theory	NOUN
ijassa-497	282	2	probab	probab	PROPN
ijassa-497	282	3	.	.	PUNCT
ijassa-497	283	1	appl	appl	PROPN
ijassa-497	283	2	.	.	PROPN
ijassa-497	283	3	,	,	PUNCT
ijassa-497	283	4	53(3	53(3	NUM
ijassa-497	283	5	)	)	PUNCT
ijassa-497	283	6	,	,	PUNCT
ijassa-497	283	7	500–518	500–518	NUM
ijassa-497	283	8	.	.	NOUN
ijassa-497	283	9	13	13	NUM
ijassa-497	283	10	.	.	PUNCT
ijassa-497	284	1	burnaev	burnaev	PROPN
ijassa-497	284	2	e.	e.	PROPN
ijassa-497	284	3	,	,	PUNCT
ijassa-497	284	4	feinberg	feinberg	PROPN
ijassa-497	284	5	e.	e.	PROPN
ijassa-497	284	6	&	&	CCONJ
ijassa-497	284	7	shiryaev	shiryaev	PROPN
ijassa-497	284	8	a.	a.	PROPN
ijassa-497	284	9	(	(	PUNCT
ijassa-497	284	10	2009	2009	NUM
ijassa-497	284	11	)	)	PUNCT
ijassa-497	284	12	on	on	ADP
ijassa-497	284	13	asymptotic	asymptotic	ADJ
ijassa-497	284	14	optimality	optimality	NOUN
ijassa-497	284	15	of	of	ADP
ijassa-497	284	16	the	the	DET
ijassa-497	284	17	second	second	ADJ
ijassa-497	284	18	order	order	NOUN
ijassa-497	284	19	in	in	ADP
ijassa-497	284	20	the	the	DET
ijassa-497	284	21	minimax	minimax	NOUN
ijassa-497	284	22	quickest	quick	ADJ
ijassa-497	284	23	detection	detection	NOUN
ijassa-497	284	24	problem	problem	NOUN
ijassa-497	284	25	of	of	ADP
ijassa-497	284	26	drift	drift	NOUN
ijassa-497	284	27	change	change	NOUN
ijassa-497	284	28	for	for	ADP
ijassa-497	284	29	brownian	brownian	ADJ
ijassa-497	284	30	motion	motion	NOUN
ijassa-497	284	31	.	.	PUNCT
ijassa-497	285	1	theory	theory	NOUN
ijassa-497	285	2	probab	probab	PROPN
ijassa-497	285	3	.	.	PUNCT
ijassa-497	286	1	appl	appl	PROPN
ijassa-497	286	2	.	.	PROPN
ijassa-497	286	3	,	,	PUNCT
ijassa-497	286	4	53(3	53(3	NUM
ijassa-497	286	5	)	)	PUNCT
ijassa-497	286	6	,	,	PUNCT
ijassa-497	286	7	519–536	519–536	NUM
ijassa-497	286	8	.	.	PUNCT
ijassa-497	287	1	14	14	NUM
ijassa-497	287	2	.	.	PUNCT
ijassa-497	288	1	schneider	schneider	PROPN
ijassa-497	288	2	m.	m.	PROPN
ijassa-497	288	3	,	,	PUNCT
ijassa-497	288	4	ertel	ertel	PROPN
ijassa-497	288	5	w.	w.	PROPN
ijassa-497	288	6	&	&	CCONJ
ijassa-497	288	7	ramos	ramos	PROPN
ijassa-497	288	8	fabio	fabio	PROPN
ijassa-497	288	9	t.	t.	PROPN
ijassa-497	288	10	(	(	PUNCT
ijassa-497	288	11	2016	2016	NUM
ijassa-497	288	12	)	)	PUNCT
ijassa-497	288	13	expected	expect	VERB
ijassa-497	288	14	similarity	similarity	NOUN
ijassa-497	288	15	estimation	estimation	NOUN
ijassa-497	288	16	for	for	ADP
ijassa-497	288	17	large	large	ADJ
ijassa-497	288	18	-	-	PUNCT
ijassa-497	288	19	scale	scale	NOUN
ijassa-497	288	20	batch	batch	NOUN
ijassa-497	288	21	and	and	CCONJ
ijassa-497	288	22	streaming	streaming	NOUN
ijassa-497	288	23	anomaly	anomaly	NOUN
ijassa-497	288	24	detection	detection	NOUN
ijassa-497	288	25	.	.	PUNCT
ijassa-497	289	1	machine	machine	NOUN
ijassa-497	289	2	learning	learning	NOUN
ijassa-497	289	3	,	,	PUNCT
ijassa-497	289	4	105(3	105(3	NUM
ijassa-497	289	5	)	)	PUNCT
ijassa-497	289	6	,	,	PUNCT
ijassa-497	289	7	305	305	NUM
ijassa-497	289	8	–	–	PUNCT
ijassa-497	289	9	333	333	NUM
ijassa-497	289	10	,	,	PUNCT
ijassa-497	289	11	https://doi.org/10.1007/s10994-016-5567-7	https://doi.org/10.1007/s10994-016-5567-7	NOUN
ijassa-497	289	12	15	15	NUM
ijassa-497	289	13	.	.	PUNCT
ijassa-497	290	1	chandola	chandola	PROPN
ijassa-497	290	2	v.	v.	PROPN
ijassa-497	290	3	,	,	PUNCT
ijassa-497	290	4	banerjee	banerjee	PROPN
ijassa-497	290	5	a.	a.	PROPN
ijassa-497	290	6	&	&	CCONJ
ijassa-497	290	7	kumar	kumar	PROPN
ijassa-497	290	8	v.	v.	PROPN
ijassa-497	290	9	(	(	PUNCT
ijassa-497	290	10	2009	2009	NUM
ijassa-497	290	11	)	)	PUNCT
ijassa-497	290	12	anomaly	anomaly	NOUN
ijassa-497	290	13	detection	detection	NOUN
ijassa-497	290	14	:	:	PUNCT
ijassa-497	290	15	a	a	DET
ijassa-497	290	16	survey	survey	NOUN
ijassa-497	290	17	.	.	PUNCT
ijassa-497	291	1	acm	acm	PROPN
ijassa-497	291	2	comput	comput	NOUN
ijassa-497	291	3	.	.	PUNCT
ijassa-497	292	1	surv	surv	PROPN
ijassa-497	292	2	.	.	PUNCT
ijassa-497	292	3	,	,	PUNCT
ijassa-497	292	4	41(3	41(3	NUM
ijassa-497	292	5	)	)	PUNCT
ijassa-497	292	6	,	,	PUNCT
ijassa-497	292	7	15:1–15:58	15:1–15:58	NUM
ijassa-497	292	8	.	.	PUNCT
ijassa-497	292	9	16	16	NUM
ijassa-497	292	10	.	.	PUNCT
ijassa-497	293	1	laxhammar	laxhammar	PROPN
ijassa-497	293	2	r.	r.	PROPN
ijassa-497	293	3	(	(	PUNCT
ijassa-497	293	4	2014	2014	NUM
ijassa-497	293	5	)	)	PUNCT
ijassa-497	293	6	conformal	conformal	NOUN
ijassa-497	293	7	anomaly	anomaly	NOUN
ijassa-497	293	8	detection	detection	NOUN
ijassa-497	293	9	.	.	PUNCT
ijassa-497	294	1	detecting	detect	VERB
ijassa-497	294	2	abnormal	abnormal	ADJ
ijassa-497	294	3	trajectories	trajectory	NOUN
ijassa-497	294	4	in	in	ADP
ijassa-497	294	5	surveillance	surveillance	NOUN
ijassa-497	294	6	applications	application	NOUN
ijassa-497	294	7	.	.	PUNCT
ijassa-497	295	1	ph	ph	PROPN
ijassa-497	295	2	.	.	PROPN
ijassa-497	295	3	d.	d.	PROPN
ijassa-497	295	4	thesis	thesis	PROPN
ijassa-497	295	5	.	.	PUNCT
ijassa-497	296	1	university	university	NOUN
ijassa-497	296	2	of	of	ADP
ijassa-497	296	3	skövde	skövde	PROPN
ijassa-497	296	4	,	,	PUNCT
ijassa-497	296	5	skövde	skövde	PROPN
ijassa-497	296	6	.	.	PUNCT
ijassa-497	297	1	retrieved	retrieve	VERB
ijassa-497	297	2	from	from	ADP
ijassa-497	297	3	http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762	http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762	PROPN
ijassa-497	297	4	17	17	NUM
ijassa-497	297	5	.	.	PUNCT
ijassa-497	298	1	pimentel	pimentel	PROPN
ijassa-497	298	2	m.	m.	PROPN
ijassa-497	298	3	a.	a.	PROPN
ijassa-497	298	4	f.	f.	PROPN
ijassa-497	298	5	,	,	PUNCT
ijassa-497	298	6	clifton	clifton	PROPN
ijassa-497	298	7	d.	d.	PROPN
ijassa-497	298	8	a.	a.	PROPN
ijassa-497	298	9	,	,	PUNCT
ijassa-497	298	10	clifton	clifton	PROPN
ijassa-497	298	11	l.	l.	PROPN
ijassa-497	298	12	&	&	CCONJ
ijassa-497	298	13	tarassenko	tarassenko	PROPN
ijassa-497	298	14	l.	l.	PROPN
ijassa-497	298	15	(	(	PUNCT
ijassa-497	298	16	2014	2014	NUM
ijassa-497	298	17	)	)	PUNCT
ijassa-497	298	18	review	review	NOUN
ijassa-497	298	19	:	:	PUNCT
ijassa-497	298	20	a	a	DET
ijassa-497	298	21	review	review	NOUN
ijassa-497	298	22	of	of	ADP
ijassa-497	298	23	novelty	novelty	NOUN
ijassa-497	298	24	detection	detection	NOUN
ijassa-497	298	25	.	.	PUNCT
ijassa-497	299	1	signal	signal	NOUN
ijassa-497	299	2	process	process	NOUN
ijassa-497	299	3	,	,	PUNCT
ijassa-497	299	4	99	99	NUM
ijassa-497	299	5	,	,	PUNCT
ijassa-497	299	6	215–249	215–249	NUM
ijassa-497	299	7	.	.	NOUN
ijassa-497	299	8	18	18	NUM
ijassa-497	299	9	.	.	PUNCT
ijassa-497	299	10	ramaswamy	ramaswamy	PROPN
ijassa-497	299	11	s.	s.	PROPN
ijassa-497	299	12	,	,	PUNCT
ijassa-497	299	13	rastogi	rastogi	PROPN
ijassa-497	299	14	r.	r.	PROPN
ijassa-497	299	15	&	&	CCONJ
ijassa-497	299	16	shim	shim	PROPN
ijassa-497	299	17	k.	k.	PROPN
ijassa-497	299	18	(	(	PUNCT
ijassa-497	299	19	2000	2000	NUM
ijassa-497	299	20	)	)	PUNCT
ijassa-497	299	21	efficient	efficient	ADJ
ijassa-497	299	22	algorithms	algorithm	NOUN
ijassa-497	299	23	for	for	ADP
ijassa-497	299	24	mining	mining	NOUN
ijassa-497	299	25	outliers	outlier	NOUN
ijassa-497	299	26	from	from	ADP
ijassa-497	299	27	large	large	ADJ
ijassa-497	299	28	data	datum	NOUN
ijassa-497	299	29	sets	set	NOUN
ijassa-497	299	30	.	.	PUNCT
ijassa-497	300	1	sigmod	sigmod	PROPN
ijassa-497	300	2	rec	rec	PROPN
ijassa-497	300	3	.	.	PROPN
ijassa-497	300	4	,	,	PUNCT
ijassa-497	300	5	29(2	29(2	NUM
ijassa-497	300	6	)	)	PUNCT
ijassa-497	300	7	,	,	PUNCT
ijassa-497	300	8	427–438	427–438	NUM
ijassa-497	300	9	.	.	NOUN
ijassa-497	300	10	19	19	NUM
ijassa-497	300	11	.	.	PUNCT
ijassa-497	300	12	breunig	breunig	PROPN
ijassa-497	300	13	m.m	m.m	AUX
ijassa-497	300	14	.	.	PROPN
ijassa-497	300	15	,	,	PUNCT
ijassa-497	300	16	kriegel	kriegel	PROPN
ijassa-497	300	17	h.-p	h.-p	PROPN
ijassa-497	300	18	.	.	PUNCT
ijassa-497	300	19	,	,	PUNCT
ijassa-497	300	20	ng	ng	PROPN
ijassa-497	300	21	r.	r.	PROPN
ijassa-497	300	22	t.	t.	PROPN
ijassa-497	300	23	&	&	CCONJ
ijassa-497	300	24	sander	sander	PROPN
ijassa-497	300	25	j.	j.	PROPN
ijassa-497	300	26	(	(	PUNCT
ijassa-497	300	27	2000	2000	NUM
ijassa-497	300	28	)	)	PUNCT
ijassa-497	300	29	lof	lof	NOUN
ijassa-497	300	30	:	:	PUNCT
ijassa-497	300	31	identifying	identify	VERB
ijassa-497	300	32	densitybased	densitybase	VERB
ijassa-497	300	33	local	local	ADJ
ijassa-497	300	34	outliers	outlier	NOUN
ijassa-497	300	35	.	.	PUNCT
ijassa-497	301	1	sigmod	sigmod	PROPN
ijassa-497	301	2	rec	rec	PROPN
ijassa-497	301	3	.	.	PROPN
ijassa-497	301	4	,	,	PUNCT
ijassa-497	301	5	29(2	29(2	NUM
ijassa-497	301	6	)	)	PUNCT
ijassa-497	301	7	,	,	PUNCT
ijassa-497	301	8	93–104	93–104	PROPN
ijassa-497	301	9	.	.	PUNCT
ijassa-497	302	1	copyright	copyright	NOUN
ijassa-497	302	2	©	©	PROPN
ijassa-497	302	3	2017	2017	NUM
ijassa-497	302	4	assa	assa	NOUN
ijassa-497	302	5	.	.	PUNCT
ijassa-497	303	1	adv	adv	PROPN
ijassa-497	303	2	syst	syst	PROPN
ijassa-497	303	3	sci	sci	PROPN
ijassa-497	303	4	appl	appl	PROPN
ijassa-497	303	5	(	(	PUNCT
ijassa-497	303	6	2017	2017	NUM
ijassa-497	303	7	)	)	PUNCT
ijassa-497	303	8	http://dx.doi.org/10.1117/12.2228794	http://dx.doi.org/10.1117/12.2228794	PUNCT
ijassa-497	303	9	http://dx.doi.org/10.1117/12.2228369	http://dx.doi.org/10.1117/12.2228369	PUNCT
ijassa-497	303	10	http://dx.doi.org/10.1117/12.2228523	http://dx.doi.org/10.1117/12.2228523	NOUN
ijassa-497	303	11	https://arxiv.org/abs/1706.02289	https://arxiv.org/abs/1706.02289	PROPN
ijassa-497	303	12	https://doi.org/10.1007/s10994-016-5567-7	https://doi.org/10.1007/s10994-016-5567-7	NUM
ijassa-497	303	13	http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762	http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762	X
ijassa-497	303	14	conformal	conformal	ADJ
ijassa-497	303	15	kernel	kernel	PROPN
ijassa-497	303	16	expected	expect	VERB
ijassa-497	303	17	similarity	similarity	NOUN
ijassa-497	303	18	for	for	ADP
ijassa-497	303	19	anomaly	anomaly	NOUN
ijassa-497	303	20	detection	detection	NOUN
ijassa-497	303	21	in	in	ADP
ijassa-497	303	22	time	time	NOUN
ijassa-497	303	23	-	-	PUNCT
ijassa-497	303	24	series	series	NOUN
ijassa-497	303	25	data	datum	NOUN
ijassa-497	303	26	33	33	NUM
ijassa-497	303	27	20	20	NUM
ijassa-497	303	28	.	.	PUNCT
ijassa-497	304	1	zhao	zhao	PROPN
ijassa-497	304	2	m.	m.	PROPN
ijassa-497	304	3	&	&	CCONJ
ijassa-497	304	4	saligrama	saligrama	PROPN
ijassa-497	305	1	v.	v.	ADP
ijassa-497	305	2	(	(	PUNCT
ijassa-497	305	3	2009	2009	NUM
ijassa-497	305	4	)	)	PUNCT
ijassa-497	305	5	anomaly	anomaly	NOUN
ijassa-497	305	6	detection	detection	NOUN
ijassa-497	305	7	with	with	ADP
ijassa-497	305	8	score	score	NOUN
ijassa-497	305	9	functions	function	NOUN
ijassa-497	305	10	based	base	VERB
ijassa-497	305	11	on	on	ADP
ijassa-497	305	12	nearest	near	ADJ
ijassa-497	305	13	neighbor	neighbor	NOUN
ijassa-497	305	14	graphs	graph	NOUN
ijassa-497	305	15	.	.	PUNCT
ijassa-497	306	1	nips’09	nips’09	PROPN
ijassa-497	306	2	proceedings	proceeding	NOUN
ijassa-497	306	3	of	of	ADP
ijassa-497	306	4	the	the	DET
ijassa-497	306	5	22nd	22nd	ADJ
ijassa-497	306	6	international	international	ADJ
ijassa-497	306	7	conference	conference	NOUN
ijassa-497	306	8	on	on	ADP
ijassa-497	306	9	neural	neural	ADJ
ijassa-497	306	10	information	information	NOUN
ijassa-497	306	11	processing	processing	NOUN
ijassa-497	306	12	systems	system	NOUN
ijassa-497	306	13	,	,	PUNCT
ijassa-497	306	14	vancouver	vancouver	PROPN
ijassa-497	306	15	,	,	PUNCT
ijassa-497	306	16	canada	canada	PROPN
ijassa-497	306	17	,	,	PUNCT
ijassa-497	306	18	2250	2250	NUM
ijassa-497	306	19	-	-	SYM
ijassa-497	306	20	2258	2258	NUM
ijassa-497	306	21	.	.	PUNCT
ijassa-497	307	1	21	21	NUM
ijassa-497	307	2	.	.	PUNCT
ijassa-497	307	3	burnaev	burnaev	PROPN
ijassa-497	307	4	e.	e.	PROPN
ijassa-497	307	5	&	&	CCONJ
ijassa-497	307	6	nazarov	nazarov	PROPN
ijassa-497	307	7	i.	i.	PROPN
ijassa-497	307	8	(	(	PUNCT
ijassa-497	307	9	2016	2016	NUM
ijassa-497	307	10	)	)	PUNCT
ijassa-497	307	11	conformalized	conformalize	VERB
ijassa-497	307	12	kernel	kernel	PROPN
ijassa-497	307	13	ridge	ridge	PROPN
ijassa-497	307	14	regression	regression	PROPN
ijassa-497	307	15	.	.	PUNCT
ijassa-497	308	1	2016	2016	NUM
ijassa-497	308	2	15th	15th	ADJ
ijassa-497	308	3	ieee	ieee	PROPN
ijassa-497	308	4	international	international	ADJ
ijassa-497	308	5	conference	conference	NOUN
ijassa-497	308	6	on	on	ADP
ijassa-497	308	7	machine	machine	NOUN
ijassa-497	308	8	learning	learning	NOUN
ijassa-497	308	9	and	and	CCONJ
ijassa-497	308	10	applications	application	NOUN
ijassa-497	308	11	(	(	PUNCT
ijassa-497	308	12	icmla	icmla	NOUN
ijassa-497	308	13	)	)	PUNCT
ijassa-497	308	14	,	,	PUNCT
ijassa-497	308	15	45	45	NUM
ijassa-497	308	16	–	–	SYM
ijassa-497	308	17	52	52	NUM
ijassa-497	308	18	,	,	PUNCT
ijassa-497	308	19	https://doi.org/10.1109/icmla.2016.0017	https://doi.org/10.1109/icmla.2016.0017	PROPN
ijassa-497	308	20	22	22	NUM
ijassa-497	308	21	.	.	PUNCT
ijassa-497	309	1	burnaev	burnaev	PROPN
ijassa-497	309	2	e.	e.	PROPN
ijassa-497	309	3	&	&	CCONJ
ijassa-497	309	4	vovk	vovk	PROPN
ijassa-497	309	5	v.	v.	PROPN
ijassa-497	309	6	(	(	PUNCT
ijassa-497	309	7	2014	2014	NUM
ijassa-497	309	8	)	)	PUNCT
ijassa-497	309	9	efficiency	efficiency	NOUN
ijassa-497	309	10	of	of	ADP
ijassa-497	309	11	conformalized	conformalized	ADJ
ijassa-497	309	12	ridge	ridge	NOUN
ijassa-497	309	13	regression	regression	NOUN
ijassa-497	309	14	.	.	PUNCT
ijassa-497	310	1	proceedings	proceeding	NOUN
ijassa-497	310	2	of	of	ADP
ijassa-497	310	3	the	the	DET
ijassa-497	310	4	twenty	twenty	NUM
ijassa-497	310	5	seventh	seventh	ADJ
ijassa-497	310	6	annual	annual	ADJ
ijassa-497	310	7	conference	conference	NOUN
ijassa-497	310	8	on	on	ADP
ijassa-497	310	9	learning	learn	VERB
ijassa-497	310	10	theory	theory	NOUN
ijassa-497	310	11	.	.	PUNCT
ijassa-497	311	1	jmlr	jmlr	ADJ
ijassa-497	311	2	:	:	PUNCT
ijassa-497	311	3	workshop	workshop	NOUN
ijassa-497	311	4	and	and	CCONJ
ijassa-497	311	5	conference	conference	NOUN
ijassa-497	311	6	proceedings	proceeding	NOUN
ijassa-497	311	7	,	,	PUNCT
ijassa-497	311	8	35	35	NUM
ijassa-497	311	9	,	,	PUNCT
ijassa-497	311	10	605–622	605–622	NUM
ijassa-497	311	11	.	.	PUNCT
ijassa-497	312	1	23	23	NUM
ijassa-497	312	2	.	.	PUNCT
ijassa-497	313	1	tax	tax	PROPN
ijassa-497	313	2	d.m.j	d.m.j	PROPN
ijassa-497	313	3	.	.	PUNCT
ijassa-497	313	4	&	&	CCONJ
ijassa-497	313	5	duin	duin	PROPN
ijassa-497	313	6	r.p.w	r.p.w	PROPN
ijassa-497	313	7	.	.	PUNCT
ijassa-497	314	1	(	(	PUNCT
ijassa-497	314	2	2004	2004	NUM
ijassa-497	314	3	)	)	PUNCT
ijassa-497	314	4	support	support	NOUN
ijassa-497	314	5	vector	vector	NOUN
ijassa-497	314	6	data	datum	NOUN
ijassa-497	314	7	description	description	NOUN
ijassa-497	314	8	.	.	PUNCT
ijassa-497	315	1	machine	machine	NOUN
ijassa-497	315	2	learning	learning	PROPN
ijassa-497	315	3	,	,	PUNCT
ijassa-497	315	4	54	54	NUM
ijassa-497	315	5	(	(	PUNCT
ijassa-497	315	6	1	1	NUM
ijassa-497	315	7	)	)	PUNCT
ijassa-497	315	8	,	,	PUNCT
ijassa-497	315	9	45–66	45–66	NUM
ijassa-497	315	10	.	.	PROPN
ijassa-497	315	11	24	24	NUM
ijassa-497	315	12	.	.	PUNCT
ijassa-497	316	1	chang	chang	PROPN
ijassa-497	316	2	w.-c	w.-c	PROPN
ijassa-497	316	3	.	.	PUNCT
ijassa-497	316	4	,	,	PUNCT
ijassa-497	316	5	lee	lee	PROPN
ijassa-497	316	6	c.-p	c.-p	PROPN
ijassa-497	316	7	.	.	PROPN
ijassa-497	316	8	&	&	CCONJ
ijassa-497	316	9	lin	lin	PROPN
ijassa-497	316	10	c.-jen	c.-jen	PROPN
ijassa-497	316	11	.	.	PUNCT
ijassa-497	317	1	(	(	PUNCT
ijassa-497	317	2	2013	2013	NUM
ijassa-497	317	3	)	)	PUNCT
ijassa-497	317	4	a	a	DET
ijassa-497	317	5	revisit	revisit	NOUN
ijassa-497	317	6	to	to	PART
ijassa-497	317	7	support	support	VERB
ijassa-497	317	8	vector	vector	NOUN
ijassa-497	317	9	data	datum	NOUN
ijassa-497	317	10	description	description	NOUN
ijassa-497	317	11	(	(	PUNCT
ijassa-497	317	12	svdd	svdd	PROPN
ijassa-497	317	13	)	)	PUNCT
ijassa-497	317	14	.	.	PUNCT
ijassa-497	318	1	documents	document	NOUN
ijassa-497	318	2	in	in	ADP
ijassa-497	318	3	the	the	DET
ijassa-497	318	4	citeseerx	citeseerx	NOUN
ijassa-497	318	5	database	database	NOUN
ijassa-497	319	1	[	[	X
ijassa-497	319	2	online	online	X
ijassa-497	319	3	]	]	X
ijassa-497	319	4	.	.	PUNCT
ijassa-497	320	1	available	available	ADJ
ijassa-497	320	2	:	:	PUNCT
ijassa-497	320	3	http://ai2-s2-pdfs.s3.amazonaws.com/a244/422ba339713d0c9eaa153b378e9f9fc08263.pdf	http://ai2-s2-pdfs.s3.amazonaws.com/a244/422ba339713d0c9eaa153b378e9f9fc08263.pdf	PROPN
ijassa-497	320	4	25	25	NUM
ijassa-497	320	5	.	.	PUNCT
ijassa-497	321	1	schölkopf	schölkopf	PROPN
ijassa-497	321	2	b.	b.	PROPN
ijassa-497	321	3	,	,	PUNCT
ijassa-497	321	4	platt	platt	PROPN
ijassa-497	321	5	j.c	j.c	PROPN
ijassa-497	321	6	.	.	PROPN
ijassa-497	321	7	,	,	PUNCT
ijassa-497	321	8	shawe	shawe	NOUN
ijassa-497	321	9	-	-	PUNCT
ijassa-497	321	10	taylor	taylor	PROPN
ijassa-497	321	11	j.	j.	PROPN
ijassa-497	321	12	c.	c.	PROPN
ijassa-497	321	13	et	et	PROPN
ijassa-497	321	14	al	al	PROPN
ijassa-497	321	15	.	.	PROPN
ijassa-497	321	16	(	(	PUNCT
ijassa-497	321	17	2001	2001	NUM
ijassa-497	321	18	)	)	PUNCT
ijassa-497	321	19	estimating	estimate	VERB
ijassa-497	321	20	the	the	DET
ijassa-497	321	21	support	support	NOUN
ijassa-497	321	22	of	of	ADP
ijassa-497	321	23	a	a	DET
ijassa-497	321	24	high	high	ADJ
ijassa-497	321	25	-	-	PUNCT
ijassa-497	321	26	dimensional	dimensional	ADJ
ijassa-497	321	27	distribution	distribution	NOUN
ijassa-497	321	28	.	.	PUNCT
ijassa-497	322	1	neural	neural	ADJ
ijassa-497	322	2	comput	comput	NOUN
ijassa-497	322	3	.	.	PUNCT
ijassa-497	323	1	,	,	PUNCT
ijassa-497	323	2	13	13	NUM
ijassa-497	323	3	(	(	PUNCT
ijassa-497	323	4	7	7	NUM
ijassa-497	323	5	)	)	PUNCT
ijassa-497	323	6	,	,	PUNCT
ijassa-497	323	7	1443–1471	1443–1471	NOUN
ijassa-497	323	8	.	.	PUNCT
ijassa-497	324	1	26	26	NUM
ijassa-497	324	2	.	.	PUNCT
ijassa-497	324	3	ma	ma	PROPN
ijassa-497	324	4	j.	j.	PROPN
ijassa-497	324	5	&	&	CCONJ
ijassa-497	324	6	perkins	perkins	PROPN
ijassa-497	324	7	s.	s.	PROPN
ijassa-497	324	8	(	(	PUNCT
ijassa-497	324	9	2003	2003	NUM
ijassa-497	324	10	)	)	PUNCT
ijassa-497	324	11	time	time	NOUN
ijassa-497	324	12	-	-	PUNCT
ijassa-497	324	13	series	series	NOUN
ijassa-497	324	14	novelty	novelty	NOUN
ijassa-497	324	15	detection	detection	NOUN
ijassa-497	324	16	using	use	VERB
ijassa-497	324	17	one	one	NUM
ijassa-497	324	18	-	-	PUNCT
ijassa-497	324	19	class	class	NOUN
ijassa-497	324	20	support	support	NOUN
ijassa-497	324	21	vector	vector	NOUN
ijassa-497	324	22	machines	machine	NOUN
ijassa-497	324	23	.	.	PUNCT
ijassa-497	325	1	proceedings	proceeding	NOUN
ijassa-497	325	2	of	of	ADP
ijassa-497	325	3	the	the	DET
ijassa-497	325	4	international	international	ADJ
ijassa-497	325	5	joint	joint	ADJ
ijassa-497	325	6	conference	conference	NOUN
ijassa-497	325	7	on	on	ADP
ijassa-497	325	8	neural	neural	ADJ
ijassa-497	325	9	networks	network	NOUN
ijassa-497	325	10	,	,	PUNCT
ijassa-497	325	11	2003	2003	NUM
ijassa-497	325	12	,	,	PUNCT
ijassa-497	325	13	3	3	NUM
ijassa-497	325	14	,	,	PUNCT
ijassa-497	325	15	1741–1745	1741–1745	NUM
ijassa-497	325	16	.	.	PUNCT
ijassa-497	325	17	27	27	NUM
ijassa-497	325	18	.	.	PUNCT
ijassa-497	326	1	laxhammar	laxhammar	PROPN
ijassa-497	326	2	r.	r.	PROPN
ijassa-497	326	3	&	&	CCONJ
ijassa-497	326	4	falkman	falkman	PROPN
ijassa-497	326	5	g.	g.	PROPN
ijassa-497	326	6	(	(	PUNCT
ijassa-497	326	7	2015	2015	NUM
ijassa-497	326	8	)	)	PUNCT
ijassa-497	326	9	inductive	inductive	VERB
ijassa-497	326	10	conformal	conformal	NOUN
ijassa-497	326	11	anomaly	anomaly	NOUN
ijassa-497	326	12	detection	detection	NOUN
ijassa-497	326	13	for	for	ADP
ijassa-497	326	14	sequential	sequential	ADJ
ijassa-497	326	15	detection	detection	NOUN
ijassa-497	326	16	of	of	ADP
ijassa-497	326	17	anomalous	anomalous	ADJ
ijassa-497	326	18	sub	sub	NOUN
ijassa-497	326	19	-	-	NOUN
ijassa-497	326	20	trajectories	trajectory	NOUN
ijassa-497	326	21	.	.	PUNCT
ijassa-497	327	1	annals	annal	NOUN
ijassa-497	327	2	of	of	ADP
ijassa-497	327	3	mathematics	mathematic	NOUN
ijassa-497	327	4	and	and	CCONJ
ijassa-497	327	5	artificial	artificial	ADJ
ijassa-497	327	6	intelligence	intelligence	NOUN
ijassa-497	327	7	,	,	PUNCT
ijassa-497	327	8	74	74	NUM
ijassa-497	327	9	(	(	PUNCT
ijassa-497	327	10	1	1	NUM
ijassa-497	327	11	)	)	PUNCT
ijassa-497	327	12	,	,	PUNCT
ijassa-497	327	13	67–94	67–94	NUM
ijassa-497	327	14	.	.	PUNCT
ijassa-497	328	1	28	28	NUM
ijassa-497	328	2	.	.	PUNCT
ijassa-497	329	1	vovk	vovk	PROPN
ijassa-497	329	2	v.	v.	ADP
ijassa-497	329	3	(	(	PUNCT
ijassa-497	329	4	2012	2012	NUM
ijassa-497	329	5	)	)	PUNCT
ijassa-497	329	6	conditional	conditional	ADJ
ijassa-497	329	7	validity	validity	NOUN
ijassa-497	329	8	of	of	ADP
ijassa-497	329	9	inductive	inductive	ADJ
ijassa-497	329	10	conformal	conformal	ADJ
ijassa-497	329	11	predictors	predictor	NOUN
ijassa-497	329	12	.	.	PUNCT
ijassa-497	330	1	proceedings	proceeding	NOUN
ijassa-497	330	2	of	of	ADP
ijassa-497	330	3	the	the	DET
ijassa-497	330	4	asian	asian	ADJ
ijassa-497	330	5	conference	conference	NOUN
ijassa-497	330	6	on	on	ADP
ijassa-497	330	7	machine	machine	NOUN
ijassa-497	330	8	learning	learning	NOUN
ijassa-497	330	9	,	,	PUNCT
ijassa-497	330	10	in	in	ADP
ijassa-497	330	11	pmlr	pmlr	NOUN
ijassa-497	330	12	,	,	PUNCT
ijassa-497	330	13	25	25	NUM
ijassa-497	330	14	,	,	PUNCT
ijassa-497	330	15	475	475	NUM
ijassa-497	330	16	-	-	SYM
ijassa-497	330	17	490	490	NUM
ijassa-497	330	18	.	.	PROPN
ijassa-497	330	19	29	29	NUM
ijassa-497	330	20	.	.	PUNCT
ijassa-497	331	1	smola	smola	PROPN
ijassa-497	331	2	a.	a.	PROPN
ijassa-497	331	3	,	,	PUNCT
ijassa-497	331	4	gretton	gretton	PROPN
ijassa-497	331	5	a.	a.	NOUN
ijassa-497	331	6	,	,	PUNCT
ijassa-497	331	7	song	song	PROPN
ijassa-497	331	8	l.	l.	PROPN
ijassa-497	331	9	&	&	CCONJ
ijassa-497	331	10	schölkopf	schölkopf	PROPN
ijassa-497	331	11	b.	b.	PROPN
ijassa-497	331	12	(	(	PUNCT
ijassa-497	331	13	2007	2007	NUM
ijassa-497	331	14	)	)	PUNCT
ijassa-497	331	15	a	a	DET
ijassa-497	331	16	hilbert	hilbert	NOUN
ijassa-497	331	17	space	space	NOUN
ijassa-497	331	18	embedding	embed	VERB
ijassa-497	331	19	for	for	ADP
ijassa-497	331	20	distributions	distribution	NOUN
ijassa-497	331	21	.	.	PUNCT
ijassa-497	332	1	algorithmic	algorithmic	ADJ
ijassa-497	332	2	learning	learning	NOUN
ijassa-497	332	3	theory	theory	NOUN
ijassa-497	332	4	:	:	PUNCT
ijassa-497	332	5	18th	18th	ADJ
ijassa-497	332	6	international	international	ADJ
ijassa-497	332	7	conference	conference	NOUN
ijassa-497	332	8	,	,	PUNCT
ijassa-497	332	9	alt	alt	VERB
ijassa-497	332	10	2007	2007	NUM
ijassa-497	332	11	,	,	PUNCT
ijassa-497	332	12	13–31	13–31	NUM
ijassa-497	332	13	,	,	PUNCT
ijassa-497	332	14	https://doi.org/10.1007/978-3-540-75225-7	https://doi.org/10.1007/978-3-540-75225-7	PROPN
ijassa-497	332	15	5	5	NUM
ijassa-497	332	16	30	30	NUM
ijassa-497	332	17	.	.	PUNCT
ijassa-497	333	1	schneider	schneider	PROPN
ijassa-497	333	2	m.	m.	PROPN
ijassa-497	333	3	(	(	PUNCT
ijassa-497	333	4	2016	2016	NUM
ijassa-497	333	5	)	)	PUNCT
ijassa-497	333	6	probability	probability	NOUN
ijassa-497	333	7	inequalities	inequality	NOUN
ijassa-497	333	8	for	for	ADP
ijassa-497	333	9	kernel	kernel	NOUN
ijassa-497	333	10	embeddings	embedding	NOUN
ijassa-497	333	11	in	in	ADP
ijassa-497	333	12	sampling	sample	VERB
ijassa-497	333	13	without	without	ADP
ijassa-497	333	14	replacement	replacement	NOUN
ijassa-497	333	15	.	.	PUNCT
ijassa-497	334	1	proceedings	proceeding	NOUN
ijassa-497	334	2	of	of	ADP
ijassa-497	334	3	machine	machine	NOUN
ijassa-497	334	4	learning	learn	VERB
ijassa-497	334	5	research	research	NOUN
ijassa-497	334	6	,	,	PUNCT
ijassa-497	334	7	66–74	66–74	NUM
ijassa-497	334	8	.	.	PROPN
ijassa-497	334	9	31	31	NUM
ijassa-497	334	10	.	.	PUNCT
ijassa-497	335	1	shafer	shafer	PROPN
ijassa-497	335	2	g.	g.	PROPN
ijassa-497	335	3	&	&	CCONJ
ijassa-497	335	4	vovk	vovk	PROPN
ijassa-497	335	5	v.	v.	PROPN
ijassa-497	335	6	(	(	PUNCT
ijassa-497	335	7	2008	2008	NUM
ijassa-497	335	8	)	)	PUNCT
ijassa-497	335	9	a	a	DET
ijassa-497	335	10	tutorial	tutorial	NOUN
ijassa-497	335	11	on	on	ADP
ijassa-497	335	12	conformal	conformal	ADJ
ijassa-497	335	13	prediction	prediction	NOUN
ijassa-497	335	14	.	.	PUNCT
ijassa-497	336	1	j.	j.	PROPN
ijassa-497	336	2	mach	mach	PROPN
ijassa-497	336	3	.	.	PUNCT
ijassa-497	337	1	learn	learn	VERB
ijassa-497	337	2	.	.	PUNCT
ijassa-497	338	1	res	re	NOUN
ijassa-497	338	2	.	.	PROPN
ijassa-497	338	3	,	,	PUNCT
ijassa-497	338	4	9	9	NUM
ijassa-497	338	5	,	,	PUNCT
ijassa-497	338	6	371–421	371–421	NUM
ijassa-497	338	7	32	32	NUM
ijassa-497	338	8	.	.	PUNCT
ijassa-497	339	1	ishimtsev	ishimtsev	PROPN
ijassa-497	339	2	v.	v.	PROPN
ijassa-497	339	3	,	,	PUNCT
ijassa-497	339	4	nazarov	nazarov	PROPN
ijassa-497	339	5	i.	i.	PROPN
ijassa-497	339	6	,	,	PUNCT
ijassa-497	339	7	bernstein	bernstein	PROPN
ijassa-497	339	8	a.	a.	PROPN
ijassa-497	339	9	&	&	CCONJ
ijassa-497	339	10	burnaev	burnaev	PROPN
ijassa-497	339	11	e.	e.	PROPN
ijassa-497	339	12	(	(	PUNCT
ijassa-497	339	13	2017	2017	NUM
ijassa-497	339	14	)	)	PUNCT
ijassa-497	339	15	conformal	conformal	NOUN
ijassa-497	339	16	k	k	PROPN
ijassa-497	339	17	-	-	PUNCT
ijassa-497	339	18	nn	nn	PROPN
ijassa-497	339	19	anomaly	anomaly	NOUN
ijassa-497	339	20	detector	detector	NOUN
ijassa-497	339	21	for	for	ADP
ijassa-497	339	22	univariate	univariate	ADJ
ijassa-497	339	23	data	datum	NOUN
ijassa-497	339	24	streams	stream	NOUN
ijassa-497	339	25	.	.	PUNCT
ijassa-497	340	1	arxiv	arxiv	PROPN
ijassa-497	340	2	e	e	PROPN
ijassa-497	340	3	-	-	NOUN
ijassa-497	340	4	prints	print	NOUN
ijassa-497	340	5	,	,	PUNCT
ijassa-497	341	1	[	[	X
ijassa-497	341	2	online	online	X
ijassa-497	341	3	]	]	X
ijassa-497	341	4	.	.	PUNCT
ijassa-497	342	1	available	available	ADJ
ijassa-497	342	2	:	:	PUNCT
ijassa-497	342	3	https://arxiv.org/abs/1706.03412	https://arxiv.org/abs/1706.03412	PROPN
ijassa-497	342	4	33	33	NUM
ijassa-497	342	5	.	.	PUNCT
ijassa-497	343	1	lavin	lavin	PROPN
ijassa-497	343	2	a.	a.	PROPN
ijassa-497	343	3	&	&	CCONJ
ijassa-497	343	4	ahmad	ahmad	PROPN
ijassa-497	343	5	s.	s.	PROPN
ijassa-497	343	6	(	(	PUNCT
ijassa-497	343	7	2015	2015	NUM
ijassa-497	343	8	)	)	PUNCT
ijassa-497	343	9	evaluating	evaluate	VERB
ijassa-497	343	10	real	real	ADJ
ijassa-497	343	11	-	-	PUNCT
ijassa-497	343	12	time	time	NOUN
ijassa-497	343	13	anomaly	anomaly	NOUN
ijassa-497	343	14	detection	detection	NOUN
ijassa-497	343	15	algorithms	algorithm	VERB
ijassa-497	343	16	the	the	DET
ijassa-497	343	17	numenta	numenta	PROPN
ijassa-497	343	18	anomaly	anomaly	NOUN
ijassa-497	343	19	benchmark	benchmark	NOUN
ijassa-497	343	20	.	.	PUNCT
ijassa-497	344	1	14th	14th	ADJ
ijassa-497	344	2	international	international	ADJ
ijassa-497	344	3	conference	conference	NOUN
ijassa-497	344	4	on	on	ADP
ijassa-497	344	5	machine	machine	NOUN
ijassa-497	344	6	learning	learning	NOUN
ijassa-497	344	7	and	and	CCONJ
ijassa-497	344	8	applications	application	NOUN
ijassa-497	344	9	(	(	PUNCT
ijassa-497	344	10	ieee	ieee	NOUN
ijassa-497	344	11	icmla	icmla	NOUN
ijassa-497	344	12	)	)	PUNCT
ijassa-497	344	13	,	,	PUNCT
ijassa-497	344	14	38	38	NUM
ijassa-497	344	15	-	-	SYM
ijassa-497	344	16	44	44	NUM
ijassa-497	344	17	,	,	PUNCT
ijassa-497	344	18	https://arxiv.org/abs/1510.03336	https://arxiv.org/abs/1510.03336	NOUN
ijassa-497	344	19	34	34	NUM
ijassa-497	344	20	.	.	PUNCT
ijassa-497	344	21	yahoo	yahoo	PROPN
ijassa-497	344	22	!	!	PUNCT
ijassa-497	344	23	webscope	webscope	NOUN
ijassa-497	344	24	(	(	PUNCT
ijassa-497	344	25	2017	2017	NUM
ijassa-497	344	26	,	,	PUNCT
ijassa-497	344	27	december	december	PROPN
ijassa-497	344	28	26	26	NUM
ijassa-497	344	29	)	)	PUNCT
ijassa-497	344	30	s5	s5	PROPN
ijassa-497	344	31	a	a	DET
ijassa-497	344	32	labeled	label	VERB
ijassa-497	344	33	anomaly	anomaly	NOUN
ijassa-497	344	34	detection	detection	NOUN
ijassa-497	344	35	dataset	dataset	NOUN
ijassa-497	344	36	,	,	PUNCT
ijassa-497	344	37	version	version	NOUN
ijassa-497	344	38	1.0	1.0	NUM
ijassa-497	344	39	.	.	PUNCT
ijassa-497	345	1	[	[	X
ijassa-497	345	2	online	online	X
ijassa-497	345	3	]	]	X
ijassa-497	345	4	.	.	PUNCT
ijassa-497	345	5	available	available	ADJ
ijassa-497	345	6	https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70	https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70	NOUN
ijassa-497	345	7	.	.	PUNCT
ijassa-497	346	1	copyright	copyright	NOUN
ijassa-497	346	2	©	©	PROPN
ijassa-497	346	3	2017	2017	NUM
ijassa-497	346	4	assa	assa	NOUN
ijassa-497	346	5	.	.	PUNCT
ijassa-497	347	1	adv	adv	PROPN
ijassa-497	347	2	syst	syst	PROPN
ijassa-497	347	3	sci	sci	PROPN
ijassa-497	347	4	appl	appl	PROPN
ijassa-497	347	5	(	(	PUNCT
ijassa-497	347	6	2017	2017	NUM
ijassa-497	347	7	)	)	PUNCT
ijassa-497	347	8	https://doi.org/10.1109/icmla.2016.0017	https://doi.org/10.1109/icmla.2016.0017	PROPN
ijassa-497	347	9	http://ai2-s2-pdfs.s3.amazonaws.com/a244/422ba339713d0c9eaa153b378e9f9fc08263.pdf	http://ai2-s2-pdfs.s3.amazonaws.com/a244/422ba339713d0c9eaa153b378e9f9fc08263.pdf	NOUN
ijassa-497	347	10	https://doi.org/10.1007/978-3-540-75225-7_5	https://doi.org/10.1007/978-3-540-75225-7_5	NUM
ijassa-497	347	11	https://arxiv.org/abs/1706.03412	https://arxiv.org/abs/1706.03412	VERB
ijassa-497	347	12	https://arxiv.org/abs/1510.03336	https://arxiv.org/abs/1510.03336	PROPN
ijassa-497	347	13	https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70	https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70	ADJ
ijassa-497	347	14	introduction	introduction	NOUN
ijassa-497	347	15	overview	overview	NOUN
ijassa-497	347	16	of	of	ADP
ijassa-497	347	17	kernel	kernel	NOUN
ijassa-497	347	18	-	-	PUNCT
ijassa-497	347	19	based	base	VERB
ijassa-497	347	20	methods	method	NOUN
ijassa-497	347	21	for	for	ADP
ijassa-497	347	22	anomaly	anomaly	NOUN
ijassa-497	347	23	detection	detection	NOUN
ijassa-497	347	24	introduction	introduction	NOUN
ijassa-497	347	25	to	to	ADP
ijassa-497	347	26	kernels	kernels	PROPN
ijassa-497	347	27	expose	expose	VERB
ijassa-497	347	28	conformal	conformal	ADJ
ijassa-497	347	29	anomaly	anomaly	NOUN
ijassa-497	347	30	detection	detection	NOUN
ijassa-497	347	31	proposed	propose	VERB
ijassa-497	347	32	approach	approach	NOUN
ijassa-497	347	33	for	for	ADP
ijassa-497	347	34	anomaly	anomaly	NOUN
ijassa-497	347	35	detection	detection	NOUN
ijassa-497	347	36	in	in	ADP
ijassa-497	347	37	time	time	NOUN
ijassa-497	347	38	series	series	PROPN
ijassa-497	347	39	data	datum	NOUN
ijassa-497	347	40	results	result	NOUN
ijassa-497	347	41	on	on	ADP
ijassa-497	347	42	numenta	numenta	ADJ
ijassa-497	347	43	anomaly	anomaly	NOUN
ijassa-497	347	44	benchmark	benchmark	NOUN
ijassa-497	347	45	datasets	dataset	NOUN
ijassa-497	347	46	scoring	scoring	NOUN
ijassa-497	347	47	algorithm	algorithm	NOUN
ijassa-497	347	48	results	result	VERB
ijassa-497	347	49	automated	automate	VERB
ijassa-497	347	50	kernel	kernel	NOUN
ijassa-497	347	51	bandwidth	bandwidth	NOUN
ijassa-497	347	52	tuning	tune	VERB
ijassa-497	347	53	conclusion	conclusion	NOUN