id	sid	tid	token	lemma	pos
ajst-26903	1	1	academic	academic	ADJ
ajst-26903	1	2	journal	journal	NOUN
ajst-26903	1	3	of	of	ADP
ajst-26903	1	4	science	science	NOUN
ajst-26903	1	5	and	and	CCONJ
ajst-26903	1	6	technology	technology	NOUN
ajst-26903	1	7	issn	issn	NOUN
ajst-26903	1	8	:	:	PUNCT
ajst-26903	1	9	2771	2771	NUM
ajst-26903	1	10	-	-	SYM
ajst-26903	1	11	3032	3032	NUM
ajst-26903	1	12	|	|	NOUN
ajst-26903	1	13	vol	vol	NOUN
ajst-26903	1	14	.	.	PROPN
ajst-26903	1	15	13	13	NUM
ajst-26903	1	16	,	,	PUNCT
ajst-26903	1	17	no	no	INTJ
ajst-26903	1	18	.	.	NOUN
ajst-26903	1	19	1	1	NUM
ajst-26903	1	20	,	,	PUNCT
ajst-26903	1	21	2024	2024	NUM
ajst-26903	1	22	158	158	NUM
ajst-26903	1	23	a	a	DET
ajst-26903	1	24	gas	gas	NOUN
ajst-26903	1	25	well	well	NOUN
ajst-26903	1	26	production	production	NOUN
ajst-26903	1	27	prediction	prediction	NOUN
ajst-26903	1	28	model	model	NOUN
ajst-26903	1	29	based	base	VERB
ajst-26903	1	30	on	on	ADP
ajst-26903	1	31	time‐convolutional	time‐convolutional	ADJ
ajst-26903	1	32	neural	neural	ADJ
ajst-26903	1	33	network	network	NOUN
ajst-26903	1	34	long	long	PROPN
ajst-26903	1	35	zhang	zhang	PROPN
ajst-26903	1	36	school	school	PROPN
ajst-26903	1	37	of	of	ADP
ajst-26903	1	38	petroleum	petroleum	NOUN
ajst-26903	1	39	engineering	engineering	NOUN
ajst-26903	1	40	,	,	PUNCT
ajst-26903	1	41	xi'an	xi'an	PROPN
ajst-26903	1	42	shiyou	shiyou	PROPN
ajst-26903	1	43	university	university	PROPN
ajst-26903	1	44	,	,	PUNCT
ajst-26903	1	45	xi'an	xi'an	PROPN
ajst-26903	1	46	,	,	PUNCT
ajst-26903	1	47	shaanxi	shaanxi	PROPN
ajst-26903	1	48	710065	710065	NUM
ajst-26903	1	49	,	,	PUNCT
ajst-26903	1	50	china	china	PROPN
ajst-26903	1	51	abstract	abstract	NOUN
ajst-26903	1	52	:	:	PUNCT
ajst-26903	1	53	this	this	DET
ajst-26903	1	54	paper	paper	NOUN
ajst-26903	1	55	explores	explore	VERB
ajst-26903	1	56	gas	gas	NOUN
ajst-26903	1	57	well	well	PROPN
ajst-26903	1	58	production	production	NOUN
ajst-26903	1	59	prediction	prediction	NOUN
ajst-26903	1	60	,	,	PUNCT
ajst-26903	1	61	a	a	DET
ajst-26903	1	62	crucial	crucial	ADJ
ajst-26903	1	63	aspect	aspect	NOUN
ajst-26903	1	64	of	of	ADP
ajst-26903	1	65	gas	gas	NOUN
ajst-26903	1	66	field	field	NOUN
ajst-26903	1	67	development	development	NOUN
ajst-26903	1	68	planning	planning	NOUN
ajst-26903	1	69	and	and	CCONJ
ajst-26903	1	70	analysis	analysis	NOUN
ajst-26903	1	71	.	.	PUNCT
ajst-26903	2	1	traditional	traditional	ADJ
ajst-26903	2	2	methods	method	NOUN
ajst-26903	2	3	like	like	ADP
ajst-26903	2	4	decline	decline	NOUN
ajst-26903	2	5	curve	curve	NOUN
ajst-26903	2	6	analysis	analysis	NOUN
ajst-26903	2	7	and	and	CCONJ
ajst-26903	2	8	numerical	numerical	PROPN
ajst-26903	2	9	simulation	simulation	NOUN
ajst-26903	2	10	have	have	VERB
ajst-26903	2	11	limitations	limitation	NOUN
ajst-26903	2	12	due	due	ADP
ajst-26903	2	13	to	to	ADP
ajst-26903	2	14	their	their	PRON
ajst-26903	2	15	reliance	reliance	NOUN
ajst-26903	2	16	on	on	ADP
ajst-26903	2	17	simple	simple	ADJ
ajst-26903	2	18	mathematical	mathematical	ADJ
ajst-26903	2	19	models	model	NOUN
ajst-26903	2	20	.	.	PUNCT
ajst-26903	3	1	in	in	ADP
ajst-26903	3	2	contrast	contrast	NOUN
ajst-26903	3	3	,	,	PUNCT
ajst-26903	3	4	machine	machine	NOUN
ajst-26903	3	5	learning	learning	NOUN
ajst-26903	3	6	offers	offer	VERB
ajst-26903	3	7	a	a	DET
ajst-26903	3	8	more	more	ADV
ajst-26903	3	9	flexible	flexible	ADJ
ajst-26903	3	10	approach	approach	NOUN
ajst-26903	3	11	.	.	PUNCT
ajst-26903	4	1	the	the	DET
ajst-26903	4	2	study	study	NOUN
ajst-26903	4	3	applies	apply	VERB
ajst-26903	4	4	a	a	DET
ajst-26903	4	5	temporal	temporal	ADJ
ajst-26903	4	6	convolutional	convolutional	ADJ
ajst-26903	4	7	network	network	NOUN
ajst-26903	4	8	(	(	PUNCT
ajst-26903	4	9	tcn	tcn	PROPN
ajst-26903	4	10	)	)	PUNCT
ajst-26903	4	11	model	model	NOUN
ajst-26903	4	12	to	to	PART
ajst-26903	4	13	predict	predict	VERB
ajst-26903	4	14	production	production	NOUN
ajst-26903	4	15	for	for	ADP
ajst-26903	4	16	two	two	NUM
ajst-26903	4	17	gas	gas	NOUN
ajst-26903	4	18	wells	well	NOUN
ajst-26903	4	19	.	.	PUNCT
ajst-26903	5	1	the	the	DET
ajst-26903	5	2	model	model	NOUN
ajst-26903	5	3	's	's	PART
ajst-26903	5	4	performance	performance	NOUN
ajst-26903	5	5	was	be	AUX
ajst-26903	5	6	evaluated	evaluate	VERB
ajst-26903	5	7	using	use	VERB
ajst-26903	5	8	mse	mse	PROPN
ajst-26903	5	9	,	,	PUNCT
ajst-26903	5	10	mae	mae	PROPN
ajst-26903	5	11	,	,	PUNCT
ajst-26903	5	12	rmse	rmse	NOUN
ajst-26903	5	13	,	,	PUNCT
ajst-26903	5	14	and	and	CCONJ
ajst-26903	5	15	r²	r²	VERB
ajst-26903	5	16	,	,	PUNCT
ajst-26903	5	17	with	with	ADP
ajst-26903	5	18	results	result	NOUN
ajst-26903	5	19	showing	show	VERB
ajst-26903	5	20	the	the	DET
ajst-26903	5	21	tcn	tcn	NOUN
ajst-26903	5	22	method	method	NOUN
ajst-26903	5	23	achieving	achieve	VERB
ajst-26903	5	24	better	well	ADJ
ajst-26903	5	25	accuracy	accuracy	NOUN
ajst-26903	5	26	.	.	PUNCT
ajst-26903	6	1	the	the	DET
ajst-26903	6	2	tcn	tcn	PROPN
ajst-26903	6	3	model	model	NOUN
ajst-26903	6	4	presents	present	VERB
ajst-26903	6	5	a	a	DET
ajst-26903	6	6	promising	promising	ADJ
ajst-26903	6	7	new	new	ADJ
ajst-26903	6	8	approach	approach	NOUN
ajst-26903	6	9	for	for	ADP
ajst-26903	6	10	gas	gas	NOUN
ajst-26903	6	11	well	well	NOUN
ajst-26903	6	12	production	production	NOUN
ajst-26903	6	13	forecasting	forecasting	NOUN
ajst-26903	6	14	.	.	PUNCT
ajst-26903	7	1	keywords	keyword	NOUN
ajst-26903	7	2	:	:	PUNCT
ajst-26903	7	3	production	production	NOUN
ajst-26903	7	4	forecasting	forecasting	NOUN
ajst-26903	7	5	;	;	PUNCT
ajst-26903	7	6	machine	machine	NOUN
ajst-26903	7	7	learning	learning	NOUN
ajst-26903	7	8	;	;	PUNCT
ajst-26903	7	9	tcn	tcn	PROPN
ajst-26903	7	10	.	.	PUNCT
ajst-26903	8	1	1	1	X
ajst-26903	8	2	.	.	X
ajst-26903	8	3	introduction	introduction	NOUN
ajst-26903	8	4	gas	gas	NOUN
ajst-26903	8	5	well	well	NOUN
ajst-26903	8	6	production	production	NOUN
ajst-26903	8	7	is	be	AUX
ajst-26903	8	8	a	a	DET
ajst-26903	8	9	key	key	ADJ
ajst-26903	8	10	objective	objective	NOUN
ajst-26903	8	11	in	in	ADP
ajst-26903	8	12	gas	gas	NOUN
ajst-26903	8	13	field	field	NOUN
ajst-26903	8	14	development	development	NOUN
ajst-26903	8	15	,	,	PUNCT
ajst-26903	8	16	and	and	CCONJ
ajst-26903	8	17	accurate	accurate	ADJ
ajst-26903	8	18	prediction	prediction	NOUN
ajst-26903	8	19	of	of	ADP
ajst-26903	8	20	production	production	NOUN
ajst-26903	8	21	changes	change	NOUN
ajst-26903	8	22	is	be	AUX
ajst-26903	8	23	essential	essential	ADJ
ajst-26903	8	24	for	for	ADP
ajst-26903	8	25	designing	design	VERB
ajst-26903	8	26	development	development	NOUN
ajst-26903	8	27	plans	plan	NOUN
ajst-26903	8	28	and	and	CCONJ
ajst-26903	8	29	conducting	conduct	VERB
ajst-26903	8	30	dynamic	dynamic	ADJ
ajst-26903	8	31	analyses	analysis	NOUN
ajst-26903	8	32	.	.	PUNCT
ajst-26903	9	1	traditional	traditional	ADJ
ajst-26903	9	2	prediction	prediction	NOUN
ajst-26903	9	3	methods	method	NOUN
ajst-26903	9	4	,	,	PUNCT
ajst-26903	9	5	such	such	ADJ
ajst-26903	9	6	as	as	ADP
ajst-26903	9	7	decline	decline	NOUN
ajst-26903	9	8	curve	curve	NOUN
ajst-26903	9	9	analysis	analysis	NOUN
ajst-26903	9	10	and	and	CCONJ
ajst-26903	9	11	numerical	numerical	PROPN
ajst-26903	9	12	simulation	simulation	PROPN
ajst-26903	9	13	,	,	PUNCT
ajst-26903	9	14	each	each	PRON
ajst-26903	9	15	have	have	VERB
ajst-26903	9	16	limitations	limitation	NOUN
ajst-26903	9	17	[	[	X
ajst-26903	9	18	1	1	NUM
ajst-26903	9	19	]	]	PUNCT
ajst-26903	9	20	.	.	PUNCT
ajst-26903	10	1	decline	decline	NOUN
ajst-26903	10	2	curve	curve	NOUN
ajst-26903	10	3	analysis	analysis	NOUN
ajst-26903	10	4	,	,	PUNCT
ajst-26903	10	5	while	while	SCONJ
ajst-26903	10	6	requiring	require	VERB
ajst-26903	10	7	minimal	minimal	ADJ
ajst-26903	10	8	data	datum	NOUN
ajst-26903	10	9	and	and	CCONJ
ajst-26903	10	10	offering	offer	VERB
ajst-26903	10	11	fast	fast	ADJ
ajst-26903	10	12	calculations	calculation	NOUN
ajst-26903	10	13	,	,	PUNCT
ajst-26903	10	14	relies	rely	VERB
ajst-26903	10	15	on	on	ADP
ajst-26903	10	16	overly	overly	ADV
ajst-26903	10	17	simplified	simplified	ADJ
ajst-26903	10	18	assumptions	assumption	NOUN
ajst-26903	10	19	,	,	PUNCT
ajst-26903	10	20	making	make	VERB
ajst-26903	10	21	it	it	PRON
ajst-26903	10	22	less	less	ADV
ajst-26903	10	23	effective	effective	ADJ
ajst-26903	10	24	for	for	ADP
ajst-26903	10	25	wells	well	NOUN
ajst-26903	10	26	with	with	ADP
ajst-26903	10	27	complex	complex	ADJ
ajst-26903	10	28	production	production	NOUN
ajst-26903	10	29	dynamics	dynamic	NOUN
ajst-26903	10	30	[	[	X
ajst-26903	10	31	2	2	NUM
ajst-26903	10	32	]	]	PUNCT
ajst-26903	10	33	.	.	PUNCT
ajst-26903	11	1	numerical	numerical	PROPN
ajst-26903	11	2	simulation	simulation	PROPN
ajst-26903	11	3	,	,	PUNCT
ajst-26903	11	4	on	on	ADP
ajst-26903	11	5	the	the	DET
ajst-26903	11	6	other	other	ADJ
ajst-26903	11	7	hand	hand	NOUN
ajst-26903	11	8	,	,	PUNCT
ajst-26903	11	9	demands	demand	VERB
ajst-26903	11	10	extensive	extensive	ADJ
ajst-26903	11	11	geological	geological	ADJ
ajst-26903	11	12	,	,	PUNCT
ajst-26903	11	13	petrophysical	petrophysical	ADJ
ajst-26903	11	14	,	,	PUNCT
ajst-26903	11	15	and	and	CCONJ
ajst-26903	11	16	reservoir	reservoir	PROPN
ajst-26903	11	17	data	data	PROPN
ajst-26903	11	18	,	,	PUNCT
ajst-26903	11	19	resulting	result	VERB
ajst-26903	11	20	in	in	ADP
ajst-26903	11	21	high	high	ADJ
ajst-26903	11	22	workloads	workload	NOUN
ajst-26903	11	23	and	and	CCONJ
ajst-26903	11	24	timeconsuming	timeconsuming	NOUN
ajst-26903	11	25	processes	process	NOUN
ajst-26903	11	26	[	[	X
ajst-26903	11	27	3	3	NUM
ajst-26903	11	28	]	]	PUNCT
ajst-26903	11	29	.	.	PUNCT
ajst-26903	12	1	in	in	ADP
ajst-26903	12	2	contrast	contrast	NOUN
ajst-26903	12	3	,	,	PUNCT
ajst-26903	12	4	temporal	temporal	ADJ
ajst-26903	12	5	convolutional	convolutional	ADJ
ajst-26903	12	6	networks	network	NOUN
ajst-26903	12	7	(	(	PUNCT
ajst-26903	12	8	tcns	tcns	PROPN
ajst-26903	12	9	)	)	PUNCT
ajst-26903	12	10	,	,	PUNCT
ajst-26903	12	11	a	a	DET
ajst-26903	12	12	deep	deep	ADJ
ajst-26903	12	13	learning	learning	NOUN
ajst-26903	12	14	model	model	NOUN
ajst-26903	12	15	designed	design	VERB
ajst-26903	12	16	for	for	ADP
ajst-26903	12	17	time	time	NOUN
ajst-26903	12	18	series	series	PROPN
ajst-26903	12	19	data	data	PROPN
ajst-26903	12	20	,	,	PUNCT
ajst-26903	12	21	can	can	AUX
ajst-26903	12	22	better	well	ADV
ajst-26903	12	23	capture	capture	VERB
ajst-26903	12	24	temporal	temporal	ADJ
ajst-26903	12	25	patterns	pattern	NOUN
ajst-26903	12	26	and	and	CCONJ
ajst-26903	12	27	enhance	enhance	VERB
ajst-26903	12	28	prediction	prediction	NOUN
ajst-26903	12	29	accuracy	accuracy	NOUN
ajst-26903	12	30	in	in	ADP
ajst-26903	12	31	time	time	NOUN
ajst-26903	12	32	-	-	PUNCT
ajst-26903	12	33	dependent	dependent	ADJ
ajst-26903	12	34	data	datum	NOUN
ajst-26903	12	35	like	like	ADP
ajst-26903	12	36	gas	gas	NOUN
ajst-26903	12	37	well	well	NOUN
ajst-26903	12	38	production	production	NOUN
ajst-26903	12	39	[	[	X
ajst-26903	12	40	4	4	NUM
ajst-26903	12	41	]	]	PUNCT
ajst-26903	12	42	.	.	PUNCT
ajst-26903	13	1	this	this	PRON
ajst-26903	13	2	makes	make	VERB
ajst-26903	13	3	tcns	tcns	NOUN
ajst-26903	13	4	a	a	DET
ajst-26903	13	5	valuable	valuable	ADJ
ajst-26903	13	6	and	and	CCONJ
ajst-26903	13	7	practical	practical	ADJ
ajst-26903	13	8	approach	approach	NOUN
ajst-26903	13	9	for	for	ADP
ajst-26903	13	10	improving	improve	VERB
ajst-26903	13	11	production	production	NOUN
ajst-26903	13	12	forecasting	forecasting	NOUN
ajst-26903	13	13	.	.	PUNCT
ajst-26903	14	1	2	2	X
ajst-26903	14	2	.	.	X
ajst-26903	14	3	temporal	temporal	ADJ
ajst-26903	14	4	convolutional	convolutional	ADJ
ajst-26903	14	5	neural	neural	ADJ
ajst-26903	14	6	network	network	NOUN
ajst-26903	14	7	2.1	2.1	NUM
ajst-26903	14	8	.	.	PUNCT
ajst-26903	14	9	basic	basic	ADJ
ajst-26903	14	10	component	component	NOUN
ajst-26903	14	11	2.1.1	2.1.1	NUM
ajst-26903	14	12	.	.	PUNCT
ajst-26903	14	13	causal	causal	ADJ
ajst-26903	14	14	convolution	convolution	NOUN
ajst-26903	14	15	causal	causal	ADJ
ajst-26903	14	16	convolution	convolution	NOUN
ajst-26903	14	17	in	in	ADP
ajst-26903	14	18	temporal	temporal	ADJ
ajst-26903	14	19	convolutional	convolutional	ADJ
ajst-26903	14	20	networks	network	NOUN
ajst-26903	14	21	(	(	PUNCT
ajst-26903	14	22	tcns	tcns	NOUN
ajst-26903	14	23	)	)	PUNCT
ajst-26903	14	24	ensures	ensure	VERB
ajst-26903	14	25	that	that	SCONJ
ajst-26903	14	26	the	the	DET
ajst-26903	14	27	model	model	NOUN
ajst-26903	14	28	processes	process	VERB
ajst-26903	14	29	time	time	NOUN
ajst-26903	14	30	series	series	PROPN
ajst-26903	14	31	data	datum	NOUN
ajst-26903	14	32	in	in	ADP
ajst-26903	14	33	a	a	DET
ajst-26903	14	34	causal	causal	ADJ
ajst-26903	14	35	order	order	NOUN
ajst-26903	14	36	.	.	PUNCT
ajst-26903	15	1	this	this	PRON
ajst-26903	15	2	means	mean	VERB
ajst-26903	15	3	that	that	SCONJ
ajst-26903	15	4	when	when	SCONJ
ajst-26903	15	5	predicting	predict	VERB
ajst-26903	15	6	the	the	DET
ajst-26903	15	7	output	output	NOUN
ajst-26903	15	8	at	at	ADP
ajst-26903	15	9	any	any	DET
ajst-26903	15	10	given	give	VERB
ajst-26903	15	11	point	point	NOUN
ajst-26903	15	12	in	in	ADP
ajst-26903	15	13	time	time	NOUN
ajst-26903	15	14	,	,	PUNCT
ajst-26903	15	15	the	the	DET
ajst-26903	15	16	model	model	NOUN
ajst-26903	15	17	relies	rely	VERB
ajst-26903	15	18	solely	solely	ADV
ajst-26903	15	19	on	on	ADP
ajst-26903	15	20	past	past	ADJ
ajst-26903	15	21	information	information	NOUN
ajst-26903	15	22	,	,	PUNCT
ajst-26903	15	23	without	without	ADP
ajst-26903	15	24	access	access	NOUN
ajst-26903	15	25	to	to	ADP
ajst-26903	15	26	future	future	ADJ
ajst-26903	15	27	data	datum	NOUN
ajst-26903	15	28	.	.	PUNCT
ajst-26903	16	1	by	by	ADP
ajst-26903	16	2	enforcing	enforce	VERB
ajst-26903	16	3	this	this	DET
ajst-26903	16	4	constraint	constraint	NOUN
ajst-26903	16	5	,	,	PUNCT
ajst-26903	16	6	tcns	tcns	PROPN
ajst-26903	16	7	prevent	prevent	NOUN
ajst-26903	16	8	data	datum	NOUN
ajst-26903	16	9	leakage	leakage	NOUN
ajst-26903	16	10	and	and	CCONJ
ajst-26903	16	11	ensure	ensure	VERB
ajst-26903	16	12	that	that	SCONJ
ajst-26903	16	13	predictions	prediction	NOUN
ajst-26903	16	14	are	be	AUX
ajst-26903	16	15	based	base	VERB
ajst-26903	16	16	strictly	strictly	ADV
ajst-26903	16	17	on	on	ADP
ajst-26903	16	18	historical	historical	ADJ
ajst-26903	16	19	data	datum	NOUN
ajst-26903	16	20	,	,	PUNCT
ajst-26903	16	21	making	make	VERB
ajst-26903	16	22	them	they	PRON
ajst-26903	16	23	particularly	particularly	ADV
ajst-26903	16	24	well	well	ADV
ajst-26903	16	25	-	-	PUNCT
ajst-26903	16	26	suited	suit	VERB
ajst-26903	16	27	for	for	ADP
ajst-26903	16	28	time	time	NOUN
ajst-26903	16	29	series	series	PROPN
ajst-26903	16	30	forecasting	forecasting	NOUN
ajst-26903	16	31	tasks	task	NOUN
ajst-26903	16	32	where	where	SCONJ
ajst-26903	16	33	preserving	preserve	VERB
ajst-26903	16	34	the	the	DET
ajst-26903	16	35	temporal	temporal	ADJ
ajst-26903	16	36	sequence	sequence	NOUN
ajst-26903	16	37	is	be	AUX
ajst-26903	16	38	essential	essential	ADJ
ajst-26903	16	39	[	[	X
ajst-26903	16	40	5	5	NUM
ajst-26903	16	41	]	]	PUNCT
ajst-26903	16	42	.	.	PUNCT
ajst-26903	17	1	in	in	ADP
ajst-26903	17	2	temporal	temporal	ADJ
ajst-26903	17	3	convolutional	convolutional	ADJ
ajst-26903	17	4	networks	network	NOUN
ajst-26903	17	5	(	(	PUNCT
ajst-26903	17	6	tcns	tcns	PROPN
ajst-26903	17	7	)	)	PUNCT
ajst-26903	17	8	,	,	PUNCT
ajst-26903	17	9	the	the	DET
ajst-26903	17	10	convolution	convolution	NOUN
ajst-26903	17	11	operation	operation	NOUN
ajst-26903	17	12	at	at	ADP
ajst-26903	17	13	time	time	NOUN
ajst-26903	17	14	step	step	NOUN
ajst-26903	17	15	t	t	PROPN
ajst-26903	17	16	is	be	AUX
ajst-26903	17	17	performed	perform	VERB
ajst-26903	17	18	only	only	ADV
ajst-26903	17	19	on	on	ADP
ajst-26903	17	20	data	datum	NOUN
ajst-26903	17	21	points	point	NOUN
ajst-26903	17	22	preceding	precede	VERB
ajst-26903	17	23	t	t	PROPN
ajst-26903	17	24	,	,	PUNCT
ajst-26903	17	25	ensuring	ensure	VERB
ajst-26903	17	26	that	that	SCONJ
ajst-26903	17	27	the	the	DET
ajst-26903	17	28	result	result	NOUN
ajst-26903	17	29	at	at	ADP
ajst-26903	17	30	t	t	PROPN
ajst-26903	17	31	is	be	AUX
ajst-26903	17	32	not	not	PART
ajst-26903	17	33	influenced	influence	VERB
ajst-26903	17	34	by	by	ADP
ajst-26903	17	35	future	future	ADJ
ajst-26903	17	36	information	information	NOUN
ajst-26903	17	37	.	.	PUNCT
ajst-26903	18	1	for	for	ADP
ajst-26903	18	2	an	an	DET
ajst-26903	18	3	input	input	NOUN
ajst-26903	18	4	time	time	NOUN
ajst-26903	18	5	series	series	NOUN
ajst-26903	18	6	[	[	X
ajst-26903	18	7	x1	x1	PROPN
ajst-26903	18	8	,	,	PUNCT
ajst-26903	18	9	x2	x2	PROPN
ajst-26903	18	10	,	,	PUNCT
ajst-26903	18	11	x3	x3	ADJ
ajst-26903	18	12	,	,	PUNCT
ajst-26903	18	13	......	......	PUNCT
ajst-26903	18	14	,	,	PUNCT
ajst-26903	18	15	xt-1	xt-1	PROPN
ajst-26903	18	16	,	,	PUNCT
ajst-26903	18	17	xt	xt	ADP
ajst-26903	18	18	]	]	PUNCT
ajst-26903	18	19	and	and	CCONJ
ajst-26903	18	20	a	a	DET
ajst-26903	18	21	convolution	convolution	NOUN
ajst-26903	18	22	kernel	kernel	NOUN
ajst-26903	18	23	f	f	PROPN
ajst-26903	19	1	=	=	PUNCT
ajst-26903	20	1	[	[	X
ajst-26903	20	2	f1，f2，	f1，f2，	PROPN
ajst-26903	20	3	......	......	PUNCT
ajst-26903	20	4	，fk-1，fk	，fk-1，fk	PROPN
ajst-26903	20	5	]	]	PUNCT
ajst-26903	20	6	of	of	ADP
ajst-26903	20	7	size	size	NOUN
ajst-26903	20	8	kt	kt	PROPN
ajst-26903	20	9	,	,	PUNCT
ajst-26903	20	10	the	the	DET
ajst-26903	20	11	convolution	convolution	NOUN
ajst-26903	20	12	operation	operation	NOUN
ajst-26903	20	13	at	at	ADP
ajst-26903	20	14	time	time	NOUN
ajst-26903	20	15	t	t	PROPN
ajst-26903	20	16	is	be	AUX
ajst-26903	20	17	defined	define	VERB
ajst-26903	20	18	as	as	ADP
ajst-26903	20	19	:	:	PUNCT
ajst-26903	20	20	𝑓	𝑓	PRON
ajst-26903	20	21	𝑥	𝑥	X
ajst-26903	20	22	∑	∑	PUNCT
ajst-26903	20	23	𝑓	𝑓	PRON
ajst-26903	20	24	𝑥	𝑥	X
ajst-26903	20	25	(	(	PUNCT
ajst-26903	20	26	1	1	X
ajst-26903	20	27	)	)	PUNCT
ajst-26903	20	28	this	this	DET
ajst-26903	20	29	operation	operation	NOUN
ajst-26903	20	30	ensures	ensure	VERB
ajst-26903	20	31	that	that	SCONJ
ajst-26903	20	32	only	only	ADV
ajst-26903	20	33	past	past	ADJ
ajst-26903	20	34	values	value	NOUN
ajst-26903	20	35	up	up	ADP
ajst-26903	20	36	to	to	ADP
ajst-26903	20	37	t	t	PROPN
ajst-26903	20	38	are	be	AUX
ajst-26903	20	39	considered	consider	VERB
ajst-26903	20	40	in	in	ADP
ajst-26903	20	41	the	the	DET
ajst-26903	20	42	computation	computation	NOUN
ajst-26903	20	43	.	.	PUNCT
ajst-26903	21	1	similar	similar	ADJ
ajst-26903	21	2	to	to	ADP
ajst-26903	21	3	standard	standard	ADJ
ajst-26903	21	4	convolutional	convolutional	ADJ
ajst-26903	21	5	neural	neural	ADJ
ajst-26903	21	6	networks	network	NOUN
ajst-26903	21	7	,	,	PUNCT
ajst-26903	21	8	tcns	tcns	NOUN
ajst-26903	21	9	employ	employ	NOUN
ajst-26903	21	10	padding	padding	NOUN
ajst-26903	21	11	(	(	PUNCT
ajst-26903	21	12	by	by	ADP
ajst-26903	21	13	adding	add	VERB
ajst-26903	21	14	zeros	zero	NOUN
ajst-26903	21	15	)	)	PUNCT
ajst-26903	21	16	to	to	PART
ajst-26903	21	17	maintain	maintain	VERB
ajst-26903	21	18	the	the	DET
ajst-26903	21	19	input	input	NOUN
ajst-26903	21	20	and	and	CCONJ
ajst-26903	21	21	output	output	NOUN
ajst-26903	21	22	sequence	sequence	NOUN
ajst-26903	21	23	lengths	length	NOUN
ajst-26903	21	24	,	,	PUNCT
ajst-26903	21	25	ensuring	ensure	VERB
ajst-26903	21	26	that	that	SCONJ
ajst-26903	21	27	the	the	DET
ajst-26903	21	28	length	length	NOUN
ajst-26903	21	29	of	of	ADP
ajst-26903	21	30	the	the	DET
ajst-26903	21	31	output	output	NOUN
ajst-26903	21	32	sequence	sequence	NOUN
ajst-26903	21	33	matches	match	VERB
ajst-26903	21	34	that	that	PRON
ajst-26903	21	35	of	of	ADP
ajst-26903	21	36	the	the	DET
ajst-26903	21	37	input	input	NOUN
ajst-26903	21	38	.	.	PUNCT
ajst-26903	22	1	2.1.2	2.1.2	X
ajst-26903	22	2	.	.	X
ajst-26903	22	3	dilation	dilation	NOUN
ajst-26903	22	4	convolution	convolution	NOUN
ajst-26903	22	5	expansion	expansion	NOUN
ajst-26903	22	6	convolution	convolution	NOUN
ajst-26903	22	7	(	(	PUNCT
ajst-26903	22	8	or	or	CCONJ
ajst-26903	22	9	dilated	dilated	ADJ
ajst-26903	22	10	convolution	convolution	NOUN
ajst-26903	22	11	)	)	PUNCT
ajst-26903	22	12	enables	enable	VERB
ajst-26903	22	13	temporal	temporal	ADJ
ajst-26903	22	14	convolutional	convolutional	ADJ
ajst-26903	22	15	networks	network	NOUN
ajst-26903	22	16	(	(	PUNCT
ajst-26903	22	17	tcns	tcns	NOUN
ajst-26903	22	18	)	)	PUNCT
ajst-26903	22	19	to	to	PART
ajst-26903	22	20	broaden	broaden	VERB
ajst-26903	22	21	their	their	PRON
ajst-26903	22	22	receptive	receptive	ADJ
ajst-26903	22	23	field	field	NOUN
ajst-26903	22	24	without	without	ADP
ajst-26903	22	25	increasing	increase	VERB
ajst-26903	22	26	the	the	DET
ajst-26903	22	27	network	network	NOUN
ajst-26903	22	28	's	's	PART
ajst-26903	22	29	depth	depth	NOUN
ajst-26903	22	30	by	by	ADP
ajst-26903	22	31	introducing	introduce	VERB
ajst-26903	22	32	a	a	DET
ajst-26903	22	33	dilation	dilation	NOUN
ajst-26903	22	34	factor	factor	NOUN
ajst-26903	22	35	into	into	ADP
ajst-26903	22	36	the	the	DET
ajst-26903	22	37	convolutional	convolutional	ADJ
ajst-26903	22	38	kernel	kernel	NOUN
ajst-26903	22	39	.	.	PUNCT
ajst-26903	23	1	this	this	DET
ajst-26903	23	2	mechanism	mechanism	NOUN
ajst-26903	23	3	allows	allow	VERB
ajst-26903	23	4	tcns	tcns	NOUN
ajst-26903	23	5	to	to	PART
ajst-26903	23	6	capture	capture	VERB
ajst-26903	23	7	long	long	ADJ
ajst-26903	23	8	-	-	PUNCT
ajst-26903	23	9	range	range	NOUN
ajst-26903	23	10	temporal	temporal	ADJ
ajst-26903	23	11	dependencies	dependency	NOUN
ajst-26903	23	12	,	,	PUNCT
ajst-26903	23	13	which	which	PRON
ajst-26903	23	14	is	be	AUX
ajst-26903	23	15	critical	critical	ADJ
ajst-26903	23	16	for	for	ADP
ajst-26903	23	17	recognizing	recognize	VERB
ajst-26903	23	18	complex	complex	ADJ
ajst-26903	23	19	patterns	pattern	NOUN
ajst-26903	23	20	in	in	ADP
ajst-26903	23	21	time	time	NOUN
ajst-26903	23	22	series	series	PROPN
ajst-26903	23	23	data	data	PROPN
ajst-26903	23	24	[	[	X
ajst-26903	23	25	6	6	NUM
ajst-26903	23	26	]	]	PUNCT
ajst-26903	23	27	.	.	PUNCT
ajst-26903	24	1	in	in	ADP
ajst-26903	24	2	a	a	DET
ajst-26903	24	3	traditional	traditional	ADJ
ajst-26903	24	4	convolutional	convolutional	ADJ
ajst-26903	24	5	network	network	NOUN
ajst-26903	24	6	with	with	ADP
ajst-26903	24	7	n	n	CCONJ
ajst-26903	24	8	layers	layer	NOUN
ajst-26903	24	9	and	and	CCONJ
ajst-26903	24	10	a	a	DET
ajst-26903	24	11	kernel	kernel	NOUN
ajst-26903	24	12	size	size	NOUN
ajst-26903	24	13	of	of	ADP
ajst-26903	24	14	k	k	PROPN
ajst-26903	24	15	,	,	PUNCT
ajst-26903	24	16	the	the	DET
ajst-26903	24	17	receptive	receptive	ADJ
ajst-26903	24	18	field	field	NOUN
ajst-26903	24	19	y	y	PROPN
ajst-26903	24	20	is	be	AUX
ajst-26903	24	21	calculated	calculate	VERB
ajst-26903	24	22	as	as	ADP
ajst-26903	24	23	:	:	PUNCT
ajst-26903	24	24	y	y	PROPN
ajst-26903	24	25	=	=	NOUN
ajst-26903	24	26	n∗(k	n∗(k	NOUN
ajst-26903	24	27	−1	−1	NOUN
ajst-26903	24	28	)	)	PUNCT
ajst-26903	25	1	+1	+1	PROPN
ajst-26903	25	2	.	.	PUNCT
ajst-26903	26	1	this	this	PRON
ajst-26903	26	2	shows	show	VERB
ajst-26903	26	3	a	a	DET
ajst-26903	26	4	linear	linear	ADJ
ajst-26903	26	5	relationship	relationship	NOUN
ajst-26903	26	6	between	between	ADP
ajst-26903	26	7	the	the	DET
ajst-26903	26	8	number	number	NOUN
ajst-26903	26	9	of	of	ADP
ajst-26903	26	10	layers	layer	NOUN
ajst-26903	26	11	and	and	CCONJ
ajst-26903	26	12	the	the	DET
ajst-26903	26	13	size	size	NOUN
ajst-26903	26	14	of	of	ADP
ajst-26903	26	15	the	the	DET
ajst-26903	26	16	receptive	receptive	ADJ
ajst-26903	26	17	field	field	NOUN
ajst-26903	26	18	.	.	PUNCT
ajst-26903	27	1	however	however	ADV
ajst-26903	27	2	,	,	PUNCT
ajst-26903	27	3	with	with	ADP
ajst-26903	27	4	expansion	expansion	NOUN
ajst-26903	27	5	convolution	convolution	NOUN
ajst-26903	27	6	,	,	PUNCT
ajst-26903	27	7	the	the	DET
ajst-26903	27	8	relationship	relationship	NOUN
ajst-26903	27	9	becomes	become	VERB
ajst-26903	27	10	exponential	exponential	ADJ
ajst-26903	27	11	,	,	PUNCT
ajst-26903	27	12	allowing	allow	VERB
ajst-26903	27	13	the	the	DET
ajst-26903	27	14	network	network	NOUN
ajst-26903	27	15	to	to	PART
ajst-26903	27	16	increase	increase	VERB
ajst-26903	27	17	the	the	DET
ajst-26903	27	18	receptive	receptive	ADJ
ajst-26903	27	19	field	field	NOUN
ajst-26903	27	20	size	size	NOUN
ajst-26903	27	21	more	more	ADV
ajst-26903	27	22	efficiently	efficiently	ADV
ajst-26903	27	23	,	,	PUNCT
ajst-26903	27	24	even	even	ADV
ajst-26903	27	25	with	with	ADP
ajst-26903	27	26	fewer	few	ADJ
ajst-26903	27	27	layers	layer	NOUN
ajst-26903	27	28	.	.	PUNCT
ajst-26903	28	1	this	this	PRON
ajst-26903	28	2	not	not	PART
ajst-26903	28	3	only	only	ADV
ajst-26903	28	4	improves	improve	VERB
ajst-26903	28	5	the	the	DET
ajst-26903	28	6	model	model	NOUN
ajst-26903	28	7	's	's	PART
ajst-26903	28	8	ability	ability	NOUN
ajst-26903	28	9	to	to	PART
ajst-26903	28	10	capture	capture	VERB
ajst-26903	28	11	long	long	ADJ
ajst-26903	28	12	-	-	PUNCT
ajst-26903	28	13	range	range	NOUN
ajst-26903	28	14	dependencies	dependency	NOUN
ajst-26903	28	15	but	but	CCONJ
ajst-26903	28	16	also	also	ADV
ajst-26903	28	17	simplifies	simplify	VERB
ajst-26903	28	18	optimization	optimization	NOUN
ajst-26903	28	19	and	and	CCONJ
ajst-26903	28	20	accelerates	accelerate	VERB
ajst-26903	28	21	convergence	convergence	NOUN
ajst-26903	28	22	.	.	PUNCT
ajst-26903	29	1	when	when	SCONJ
ajst-26903	29	2	expansion	expansion	NOUN
ajst-26903	29	3	convolution	convolution	NOUN
ajst-26903	29	4	is	be	AUX
ajst-26903	29	5	applied	apply	VERB
ajst-26903	29	6	,	,	PUNCT
ajst-26903	29	7	the	the	DET
ajst-26903	29	8	receptive	receptive	ADJ
ajst-26903	29	9	field	field	NOUN
ajst-26903	29	10	size	size	NOUN
ajst-26903	29	11	y	y	PROPN
ajst-26903	29	12	changes	change	NOUN
ajst-26903	29	13	,	,	PUNCT
ajst-26903	29	14	and	and	CCONJ
ajst-26903	29	15	the	the	DET
ajst-26903	29	16	operation	operation	NOUN
ajst-26903	29	17	becomes	become	VERB
ajst-26903	29	18	:	:	PUNCT
ajst-26903	29	19	𝑓	𝑓	PRON
ajst-26903	29	20	𝑥	𝑥	X
ajst-26903	29	21	∑	∑	PUNCT
ajst-26903	29	22	  	  	SPACE
ajst-26903	29	23	𝑓	𝑓	PRON
ajst-26903	29	24	𝑥	𝑥	X
ajst-26903	29	25	(	(	PUNCT
ajst-26903	29	26	2	2	NUM
ajst-26903	29	27	)	)	PUNCT
ajst-26903	29	28	where	where	SCONJ
ajst-26903	29	29	𝑑	𝑑	NOUN
ajst-26903	29	30	is	be	AUX
ajst-26903	29	31	the	the	DET
ajst-26903	29	32	dilation	dilation	NOUN
ajst-26903	29	33	factor	factor	NOUN
ajst-26903	29	34	for	for	ADP
ajst-26903	29	35	the	the	DET
ajst-26903	29	36	n	n	CCONJ
ajst-26903	29	37	-	-	PUNCT
ajst-26903	29	38	th	th	VERB
ajst-26903	29	39	layer	layer	NOUN
ajst-26903	29	40	,	,	PUNCT
ajst-26903	29	41	calculated	calculate	VERB
ajst-26903	29	42	as	as	ADP
ajst-26903	29	43	:	:	PUNCT
ajst-26903	29	44	𝑑	𝑑	PROPN
ajst-26903	29	45	2	2	NUM
ajst-26903	29	46	(	(	PUNCT
ajst-26903	29	47	3	3	NUM
ajst-26903	29	48	)	)	PUNCT
ajst-26903	29	49	159	159	NUM
ajst-26903	29	50	this	this	DET
ajst-26903	29	51	exponential	exponential	ADJ
ajst-26903	29	52	growth	growth	NOUN
ajst-26903	29	53	in	in	ADP
ajst-26903	29	54	the	the	DET
ajst-26903	29	55	dilation	dilation	NOUN
ajst-26903	29	56	factor	factor	NOUN
ajst-26903	29	57	allows	allow	VERB
ajst-26903	29	58	the	the	DET
ajst-26903	29	59	model	model	NOUN
ajst-26903	29	60	to	to	PART
ajst-26903	29	61	effectively	effectively	ADV
ajst-26903	29	62	handle	handle	VERB
ajst-26903	29	63	long	long	ADJ
ajst-26903	29	64	sequences	sequence	NOUN
ajst-26903	29	65	and	and	CCONJ
ajst-26903	29	66	complex	complex	ADJ
ajst-26903	29	67	dependencies	dependency	NOUN
ajst-26903	29	68	,	,	PUNCT
ajst-26903	29	69	all	all	PRON
ajst-26903	29	70	while	while	SCONJ
ajst-26903	29	71	maintaining	maintain	VERB
ajst-26903	29	72	a	a	DET
ajst-26903	29	73	relatively	relatively	ADV
ajst-26903	29	74	shallow	shallow	ADJ
ajst-26903	29	75	network	network	NOUN
ajst-26903	29	76	architecture	architecture	NOUN
ajst-26903	29	77	.	.	PUNCT
ajst-26903	30	1	2.1.3	2.1.3	NUM
ajst-26903	30	2	.	.	PUNCT
ajst-26903	30	3	residual	residual	ADJ
ajst-26903	30	4	connections	connection	NOUN
ajst-26903	30	5	shortcut	shortcut	NOUN
ajst-26903	30	6	connections	connection	NOUN
ajst-26903	30	7	in	in	ADP
ajst-26903	30	8	the	the	DET
ajst-26903	30	9	residual	residual	ADJ
ajst-26903	30	10	block	block	NOUN
ajst-26903	30	11	allow	allow	VERB
ajst-26903	30	12	the	the	DET
ajst-26903	30	13	input	input	NOUN
ajst-26903	30	14	to	to	PART
ajst-26903	30	15	bypass	bypass	VERB
ajst-26903	30	16	the	the	DET
ajst-26903	30	17	convolutional	convolutional	ADJ
ajst-26903	30	18	layer	layer	NOUN
ajst-26903	30	19	and	and	CCONJ
ajst-26903	30	20	be	be	AUX
ajst-26903	30	21	directly	directly	ADV
ajst-26903	30	22	added	add	VERB
ajst-26903	30	23	to	to	ADP
ajst-26903	30	24	the	the	DET
ajst-26903	30	25	output	output	NOUN
ajst-26903	30	26	,	,	PUNCT
ajst-26903	30	27	mitigating	mitigate	VERB
ajst-26903	30	28	the	the	DET
ajst-26903	30	29	gradient	gradient	NOUN
ajst-26903	30	30	vanishing	vanishing	NOUN
ajst-26903	30	31	problem	problem	NOUN
ajst-26903	30	32	and	and	CCONJ
ajst-26903	30	33	facilitating	facilitate	VERB
ajst-26903	30	34	the	the	DET
ajst-26903	30	35	training	training	NOUN
ajst-26903	30	36	of	of	ADP
ajst-26903	30	37	deeper	deep	ADJ
ajst-26903	30	38	networks	network	NOUN
ajst-26903	30	39	.	.	PUNCT
ajst-26903	31	1	this	this	DET
ajst-26903	31	2	approach	approach	NOUN
ajst-26903	31	3	helps	help	VERB
ajst-26903	31	4	improve	improve	VERB
ajst-26903	31	5	both	both	DET
ajst-26903	31	6	optimization	optimization	NOUN
ajst-26903	31	7	and	and	CCONJ
ajst-26903	31	8	convergence	convergence	NOUN
ajst-26903	31	9	.	.	PUNCT
ajst-26903	32	1	the	the	DET
ajst-26903	32	2	residual	residual	ADJ
ajst-26903	32	3	connection	connection	NOUN
ajst-26903	32	4	can	can	AUX
ajst-26903	32	5	be	be	AUX
ajst-26903	32	6	mathematically	mathematically	ADV
ajst-26903	32	7	expressed	express	VERB
ajst-26903	32	8	as	as	ADP
ajst-26903	32	9	:	:	PUNCT
ajst-26903	32	10	𝑂𝑢𝑡𝑝𝑢𝑡	𝑂𝑢𝑡𝑝𝑢𝑡	PROPN
ajst-26903	32	11	𝑅𝑒𝐿𝑈	𝑅𝑒𝐿𝑈	VERB
ajst-26903	32	12	𝐼𝑛𝑝𝑢𝑡	𝐼𝑛𝑝𝑢𝑡	PROPN
ajst-26903	32	13	𝐹	𝐹	PROPN
ajst-26903	32	14	𝐼𝑛𝑝𝑢𝑡	𝐼𝑛𝑝𝑢𝑡	PROPN
ajst-26903	32	15	(	(	PUNCT
ajst-26903	32	16	4	4	NUM
ajst-26903	32	17	)	)	PUNCT
ajst-26903	32	18	where	where	SCONJ
ajst-26903	32	19	f(input	f(input	NOUN
ajst-26903	32	20	)	)	PUNCT
ajst-26903	32	21	represents	represent	VERB
ajst-26903	32	22	the	the	DET
ajst-26903	32	23	transformation	transformation	NOUN
ajst-26903	32	24	applied	apply	VERB
ajst-26903	32	25	to	to	ADP
ajst-26903	32	26	the	the	DET
ajst-26903	32	27	input	input	NOUN
ajst-26903	32	28	through	through	ADP
ajst-26903	32	29	the	the	DET
ajst-26903	32	30	convolutional	convolutional	ADJ
ajst-26903	32	31	layer	layer	NOUN
ajst-26903	32	32	and	and	CCONJ
ajst-26903	32	33	its	its	PRON
ajst-26903	32	34	activation	activation	NOUN
ajst-26903	32	35	function	function	NOUN
ajst-26903	32	36	.	.	PUNCT
ajst-26903	33	1	the	the	DET
ajst-26903	33	2	input	input	NOUN
ajst-26903	33	3	is	be	AUX
ajst-26903	33	4	then	then	ADV
ajst-26903	33	5	summed	sum	VERB
ajst-26903	33	6	with	with	ADP
ajst-26903	33	7	this	this	DET
ajst-26903	33	8	transformed	transform	VERB
ajst-26903	33	9	result	result	NOUN
ajst-26903	33	10	,	,	PUNCT
ajst-26903	33	11	and	and	CCONJ
ajst-26903	33	12	a	a	DET
ajst-26903	33	13	relu	relu	NOUN
ajst-26903	33	14	activation	activation	NOUN
ajst-26903	33	15	is	be	AUX
ajst-26903	33	16	applied	apply	VERB
ajst-26903	33	17	to	to	PART
ajst-26903	33	18	generate	generate	VERB
ajst-26903	33	19	the	the	DET
ajst-26903	33	20	final	final	ADJ
ajst-26903	33	21	output	output	NOUN
ajst-26903	33	22	.	.	PUNCT
ajst-26903	34	1	this	this	DET
ajst-26903	34	2	mechanism	mechanism	NOUN
ajst-26903	34	3	allows	allow	VERB
ajst-26903	34	4	the	the	DET
ajst-26903	34	5	network	network	NOUN
ajst-26903	34	6	to	to	PART
ajst-26903	34	7	preserve	preserve	VERB
ajst-26903	34	8	information	information	NOUN
ajst-26903	34	9	from	from	ADP
ajst-26903	34	10	earlier	early	ADJ
ajst-26903	34	11	layers	layer	NOUN
ajst-26903	34	12	,	,	PUNCT
ajst-26903	34	13	aiding	aid	VERB
ajst-26903	34	14	the	the	DET
ajst-26903	34	15	training	training	NOUN
ajst-26903	34	16	process	process	NOUN
ajst-26903	34	17	,	,	PUNCT
ajst-26903	34	18	particularly	particularly	ADV
ajst-26903	34	19	in	in	ADP
ajst-26903	34	20	deeper	deep	ADJ
ajst-26903	34	21	architectures	architecture	NOUN
ajst-26903	34	22	.	.	PUNCT
ajst-26903	35	1	2.2	2.2	NUM
ajst-26903	35	2	.	.	PUNCT
ajst-26903	35	3	assessment	assessment	NOUN
ajst-26903	35	4	of	of	ADP
ajst-26903	35	5	indicators	indicator	NOUN
ajst-26903	35	6	in	in	ADP
ajst-26903	35	7	this	this	DET
ajst-26903	35	8	paper	paper	NOUN
ajst-26903	35	9	,	,	PUNCT
ajst-26903	35	10	the	the	DET
ajst-26903	35	11	performance	performance	NOUN
ajst-26903	35	12	of	of	ADP
ajst-26903	35	13	the	the	DET
ajst-26903	35	14	model	model	NOUN
ajst-26903	35	15	is	be	AUX
ajst-26903	35	16	evaluated	evaluate	VERB
ajst-26903	35	17	using	use	VERB
ajst-26903	35	18	four	four	NUM
ajst-26903	35	19	metrics	metric	NOUN
ajst-26903	35	20	:	:	PUNCT
ajst-26903	35	21	mean	mean	VERB
ajst-26903	35	22	squared	square	VERB
ajst-26903	35	23	percentage	percentage	NOUN
ajst-26903	35	24	error	error	NOUN
ajst-26903	35	25	(	(	PUNCT
ajst-26903	35	26	mspe	mspe	PROPN
ajst-26903	35	27	)	)	PUNCT
ajst-26903	35	28	,	,	PUNCT
ajst-26903	35	29	root	root	NOUN
ajst-26903	35	30	mean	mean	VERB
ajst-26903	35	31	squared	square	VERB
ajst-26903	35	32	error	error	NOUN
ajst-26903	35	33	(	(	PUNCT
ajst-26903	35	34	rmse	rmse	NOUN
ajst-26903	35	35	)	)	PUNCT
ajst-26903	35	36	,	,	PUNCT
ajst-26903	35	37	mean	mean	VERB
ajst-26903	35	38	absolute	absolute	ADJ
ajst-26903	35	39	error	error	NOUN
ajst-26903	35	40	(	(	PUNCT
ajst-26903	35	41	mae	mae	PROPN
ajst-26903	35	42	)	)	PUNCT
ajst-26903	35	43	,	,	PUNCT
ajst-26903	35	44	and	and	CCONJ
ajst-26903	35	45	the	the	DET
ajst-26903	35	46	coefficient	coefficient	NOUN
ajst-26903	35	47	of	of	ADP
ajst-26903	35	48	determination	determination	NOUN
ajst-26903	35	49	(	(	PUNCT
ajst-26903	35	50	r2	r2	PROPN
ajst-26903	35	51	)	)	PUNCT
ajst-26903	35	52	.	.	PUNCT
ajst-26903	36	1	mspe	mspe	NOUN
ajst-26903	36	2	measures	measure	NOUN
ajst-26903	36	3	the	the	DET
ajst-26903	36	4	average	average	NOUN
ajst-26903	36	5	of	of	ADP
ajst-26903	36	6	the	the	DET
ajst-26903	36	7	squared	squared	ADJ
ajst-26903	36	8	relative	relative	ADJ
ajst-26903	36	9	errors	error	NOUN
ajst-26903	36	10	between	between	ADP
ajst-26903	36	11	the	the	DET
ajst-26903	36	12	predicted	predict	VERB
ajst-26903	36	13	and	and	CCONJ
ajst-26903	36	14	actual	actual	ADJ
ajst-26903	36	15	values	value	NOUN
ajst-26903	36	16	,	,	PUNCT
ajst-26903	36	17	providing	provide	VERB
ajst-26903	36	18	insight	insight	NOUN
ajst-26903	36	19	into	into	ADP
ajst-26903	36	20	the	the	DET
ajst-26903	36	21	percentage	percentage	NOUN
ajst-26903	36	22	error	error	NOUN
ajst-26903	36	23	.	.	PUNCT
ajst-26903	37	1	rmse	rmse	PROPN
ajst-26903	37	2	quantifies	quantify	VERB
ajst-26903	37	3	the	the	DET
ajst-26903	37	4	deviation	deviation	NOUN
ajst-26903	37	5	between	between	ADP
ajst-26903	37	6	predicted	predict	VERB
ajst-26903	37	7	and	and	CCONJ
ajst-26903	37	8	true	true	ADJ
ajst-26903	37	9	values	value	NOUN
ajst-26903	37	10	,	,	PUNCT
ajst-26903	37	11	reflecting	reflect	VERB
ajst-26903	37	12	how	how	SCONJ
ajst-26903	37	13	far	far	ADV
ajst-26903	37	14	off	off	ADP
ajst-26903	37	15	predictions	prediction	NOUN
ajst-26903	37	16	are	be	AUX
ajst-26903	37	17	on	on	ADP
ajst-26903	37	18	average	average	ADJ
ajst-26903	37	19	.	.	PUNCT
ajst-26903	38	1	mae	mae	PROPN
ajst-26903	38	2	calculates	calculate	VERB
ajst-26903	38	3	the	the	DET
ajst-26903	38	4	average	average	NOUN
ajst-26903	38	5	of	of	ADP
ajst-26903	38	6	the	the	DET
ajst-26903	38	7	absolute	absolute	ADJ
ajst-26903	38	8	differences	difference	NOUN
ajst-26903	38	9	between	between	ADP
ajst-26903	38	10	predicted	predict	VERB
ajst-26903	38	11	and	and	CCONJ
ajst-26903	38	12	true	true	ADJ
ajst-26903	38	13	values	value	NOUN
ajst-26903	38	14	,	,	PUNCT
ajst-26903	38	15	focusing	focus	VERB
ajst-26903	38	16	on	on	ADP
ajst-26903	38	17	the	the	DET
ajst-26903	38	18	magnitude	magnitude	NOUN
ajst-26903	38	19	of	of	ADP
ajst-26903	38	20	errors	error	NOUN
ajst-26903	38	21	.	.	PUNCT
ajst-26903	39	1	for	for	ADP
ajst-26903	39	2	these	these	DET
ajst-26903	39	3	three	three	NUM
ajst-26903	39	4	metrics	metric	NOUN
ajst-26903	39	5	,	,	PUNCT
ajst-26903	39	6	smaller	small	ADJ
ajst-26903	39	7	values	value	NOUN
ajst-26903	39	8	indicate	indicate	VERB
ajst-26903	39	9	higher	high	ADJ
ajst-26903	39	10	prediction	prediction	NOUN
ajst-26903	39	11	accuracy	accuracy	NOUN
ajst-26903	39	12	,	,	PUNCT
ajst-26903	39	13	with	with	ADP
ajst-26903	39	14	closer	close	ADJ
ajst-26903	39	15	predictions	prediction	NOUN
ajst-26903	39	16	to	to	ADP
ajst-26903	39	17	the	the	DET
ajst-26903	39	18	true	true	ADJ
ajst-26903	39	19	values	value	NOUN
ajst-26903	39	20	.	.	PUNCT
ajst-26903	40	1	their	their	PRON
ajst-26903	40	2	formulas	formula	NOUN
ajst-26903	40	3	are	be	AUX
ajst-26903	40	4	as	as	SCONJ
ajst-26903	40	5	follows	follow	VERB
ajst-26903	40	6	:	:	PUNCT
ajst-26903	40	7	𝑀𝑆𝑃𝐸	𝑀𝑆𝑃𝐸	VERB
ajst-26903	40	8	∑	∑	PROPN
ajst-26903	40	9	(	(	PUNCT
ajst-26903	40	10	5	5	NUM
ajst-26903	40	11	)	)	PUNCT
ajst-26903	40	12	𝑅𝑀𝑆𝐸	𝑅𝑀𝑆𝐸	NOUN
ajst-26903	40	13	∑	∑	PUNCT
ajst-26903	40	14	𝑦	𝑦	PRON
ajst-26903	40	15	𝑦	𝑦	X
ajst-26903	40	16	(	(	PUNCT
ajst-26903	40	17	6	6	NUM
ajst-26903	40	18	)	)	PUNCT
ajst-26903	40	19	𝑀𝐴𝐸	𝑀𝐴𝐸	PROPN
ajst-26903	40	20	∑	∑	PUNCT
ajst-26903	40	21	|	|	ADV
ajst-26903	40	22	𝑦	𝑦	NUM
ajst-26903	40	23	𝑦	𝑦	NOUN
ajst-26903	40	24	|	|	NOUN
ajst-26903	40	25	(	(	PUNCT
ajst-26903	40	26	7	7	NUM
ajst-26903	40	27	)	)	PUNCT
ajst-26903	40	28	where	where	SCONJ
ajst-26903	40	29	mmm	mmm	ADV
ajst-26903	40	30	is	be	AUX
ajst-26903	40	31	the	the	DET
ajst-26903	40	32	number	number	NOUN
ajst-26903	40	33	of	of	ADP
ajst-26903	40	34	gas	gas	NOUN
ajst-26903	40	35	well	well	NOUN
ajst-26903	40	36	data	datum	NOUN
ajst-26903	40	37	points	point	NOUN
ajst-26903	40	38	,	,	PUNCT
ajst-26903	40	39	𝑦	𝑦	NOUN
ajst-26903	40	40	is	be	AUX
ajst-26903	40	41	the	the	DET
ajst-26903	40	42	true	true	ADJ
ajst-26903	40	43	value	value	NOUN
ajst-26903	40	44	of	of	ADP
ajst-26903	40	45	gas	gas	NOUN
ajst-26903	40	46	well	well	NOUN
ajst-26903	40	47	production	production	NOUN
ajst-26903	40	48	,	,	PUNCT
ajst-26903	40	49	and	and	CCONJ
ajst-26903	40	50	𝑦	𝑦	NOUN
ajst-26903	40	51	is	be	AUX
ajst-26903	40	52	the	the	DET
ajst-26903	40	53	predicted	predict	VERB
ajst-26903	40	54	value	value	NOUN
ajst-26903	40	55	.	.	PUNCT
ajst-26903	41	1	the	the	DET
ajst-26903	41	2	coefficient	coefficient	NOUN
ajst-26903	41	3	of	of	ADP
ajst-26903	41	4	determination	determination	NOUN
ajst-26903	41	5	(	(	PUNCT
ajst-26903	41	6	𝑅2	𝑅2	NOUN
ajst-26903	41	7	)	)	PUNCT
ajst-26903	41	8	measures	measure	NOUN
ajst-26903	41	9	the	the	DET
ajst-26903	41	10	goodness	goodness	NOUN
ajst-26903	41	11	of	of	ADP
ajst-26903	41	12	fit	fit	NOUN
ajst-26903	41	13	of	of	ADP
ajst-26903	41	14	the	the	DET
ajst-26903	41	15	regression	regression	NOUN
ajst-26903	41	16	model	model	NOUN
ajst-26903	41	17	.	.	PUNCT
ajst-26903	42	1	it	it	PRON
ajst-26903	42	2	represents	represent	VERB
ajst-26903	42	3	the	the	DET
ajst-26903	42	4	proportion	proportion	NOUN
ajst-26903	42	5	of	of	ADP
ajst-26903	42	6	variance	variance	NOUN
ajst-26903	42	7	in	in	ADP
ajst-26903	42	8	the	the	DET
ajst-26903	42	9	true	true	ADJ
ajst-26903	42	10	values	value	NOUN
ajst-26903	42	11	that	that	PRON
ajst-26903	42	12	is	be	AUX
ajst-26903	42	13	explained	explain	VERB
ajst-26903	42	14	by	by	ADP
ajst-26903	42	15	the	the	DET
ajst-26903	42	16	predictions	prediction	NOUN
ajst-26903	42	17	.	.	PUNCT
ajst-26903	43	1	the	the	PRON
ajst-26903	43	2	closer	close	ADJ
ajst-26903	43	3	𝑅2	𝑅2	NOUN
ajst-26903	43	4	is	be	AUX
ajst-26903	43	5	to	to	ADP
ajst-26903	43	6	1	1	NUM
ajst-26903	43	7	,	,	PUNCT
ajst-26903	43	8	the	the	PRON
ajst-26903	43	9	better	well	ADJ
ajst-26903	43	10	the	the	DET
ajst-26903	43	11	model	model	NOUN
ajst-26903	43	12	’s	’s	PART
ajst-26903	43	13	fit	fit	NOUN
ajst-26903	43	14	.	.	PUNCT
ajst-26903	44	1	the	the	DET
ajst-26903	44	2	formula	formula	NOUN
ajst-26903	44	3	for	for	ADP
ajst-26903	44	4	𝑅2	𝑅2	NOUN
ajst-26903	44	5	is	be	AUX
ajst-26903	44	6	:	:	PUNCT
ajst-26903	44	7	𝑅	𝑅	PROPN
ajst-26903	44	8	1	1	NUM
ajst-26903	44	9	∑	∑	PROPN
ajst-26903	44	10	∑	∑	PROPN
ajst-26903	44	11	‾	‾	NOUN
ajst-26903	44	12	(	(	PUNCT
ajst-26903	44	13	8)	8)	NUM
ajst-26903	44	14	where	where	SCONJ
ajst-26903	44	15	𝑦‾	𝑦‾	NOUN
ajst-26903	44	16	is	be	AUX
ajst-26903	44	17	the	the	DET
ajst-26903	44	18	average	average	NOUN
ajst-26903	44	19	of	of	ADP
ajst-26903	44	20	the	the	DET
ajst-26903	44	21	true	true	ADJ
ajst-26903	44	22	production	production	NOUN
ajst-26903	44	23	values	value	NOUN
ajst-26903	44	24	.	.	PUNCT
ajst-26903	45	1	3	3	X
ajst-26903	45	2	.	.	X
ajst-26903	45	3	model	model	PROPN
ajst-26903	45	4	3.1	3.1	NUM
ajst-26903	45	5	.	.	PUNCT
ajst-26903	45	6	model	model	NOUN
ajst-26903	45	7	buding	bud	VERB
ajst-26903	45	8	1	1	NUM
ajst-26903	45	9	.	.	PUNCT
ajst-26903	46	1	dividing	divide	VERB
ajst-26903	46	2	the	the	DET
ajst-26903	46	3	dataset	dataset	NOUN
ajst-26903	46	4	:	:	PUNCT
ajst-26903	46	5	the	the	DET
ajst-26903	46	6	dataset	dataset	NOUN
ajst-26903	46	7	is	be	AUX
ajst-26903	46	8	split	split	VERB
ajst-26903	46	9	into	into	ADP
ajst-26903	46	10	a	a	DET
ajst-26903	46	11	training	training	NOUN
ajst-26903	46	12	set	set	NOUN
ajst-26903	46	13	and	and	CCONJ
ajst-26903	46	14	a	a	DET
ajst-26903	46	15	test	test	NOUN
ajst-26903	46	16	set	set	NOUN
ajst-26903	46	17	,	,	PUNCT
ajst-26903	46	18	with	with	ADP
ajst-26903	46	19	the	the	DET
ajst-26903	46	20	first	first	ADJ
ajst-26903	46	21	80	80	NUM
ajst-26903	46	22	%	%	NOUN
ajst-26903	46	23	of	of	ADP
ajst-26903	46	24	the	the	DET
ajst-26903	46	25	data	datum	NOUN
ajst-26903	46	26	reserved	reserve	VERB
ajst-26903	46	27	for	for	ADP
ajst-26903	46	28	training	training	NOUN
ajst-26903	46	29	and	and	CCONJ
ajst-26903	46	30	the	the	DET
ajst-26903	46	31	remaining	remain	VERB
ajst-26903	46	32	20	20	NUM
ajst-26903	46	33	%	%	NOUN
ajst-26903	46	34	for	for	ADP
ajst-26903	46	35	testing	testing	NOUN
ajst-26903	46	36	.	.	PUNCT
ajst-26903	47	1	the	the	DET
ajst-26903	47	2	training	training	NOUN
ajst-26903	47	3	set	set	NOUN
ajst-26903	47	4	is	be	AUX
ajst-26903	47	5	used	use	VERB
ajst-26903	47	6	to	to	PART
ajst-26903	47	7	train	train	VERB
ajst-26903	47	8	the	the	DET
ajst-26903	47	9	model	model	NOUN
ajst-26903	47	10	,	,	PUNCT
ajst-26903	47	11	while	while	SCONJ
ajst-26903	47	12	the	the	DET
ajst-26903	47	13	test	test	NOUN
ajst-26903	47	14	set	set	NOUN
ajst-26903	47	15	evaluates	evaluate	VERB
ajst-26903	47	16	its	its	PRON
ajst-26903	47	17	generalization	generalization	NOUN
ajst-26903	47	18	ability	ability	NOUN
ajst-26903	47	19	.	.	PUNCT
ajst-26903	48	1	2	2	X
ajst-26903	48	2	.	.	X
ajst-26903	48	3	constructing	construct	VERB
ajst-26903	48	4	the	the	DET
ajst-26903	48	5	tcn	tcn	ADJ
ajst-26903	48	6	model	model	NOUN
ajst-26903	48	7	architecture	architecture	NOUN
ajst-26903	48	8	:	:	PUNCT
ajst-26903	48	9	the	the	DET
ajst-26903	48	10	tcn	tcn	NOUN
ajst-26903	48	11	model	model	NOUN
ajst-26903	48	12	consists	consist	VERB
ajst-26903	48	13	of	of	ADP
ajst-26903	48	14	an	an	DET
ajst-26903	48	15	input	input	NOUN
ajst-26903	48	16	layer	layer	NOUN
ajst-26903	48	17	,	,	PUNCT
ajst-26903	48	18	a	a	DET
ajst-26903	48	19	tcn	tcn	NOUN
ajst-26903	48	20	layer	layer	NOUN
ajst-26903	48	21	,	,	PUNCT
ajst-26903	48	22	and	and	CCONJ
ajst-26903	48	23	an	an	DET
ajst-26903	48	24	output	output	NOUN
ajst-26903	48	25	layer	layer	NOUN
ajst-26903	48	26	.	.	PUNCT
ajst-26903	49	1	the	the	DET
ajst-26903	49	2	input	input	NOUN
ajst-26903	49	3	layer	layer	NOUN
ajst-26903	49	4	has	have	VERB
ajst-26903	49	5	a	a	DET
ajst-26903	49	6	shape	shape	NOUN
ajst-26903	49	7	that	that	PRON
ajst-26903	49	8	matches	match	VERB
ajst-26903	49	9	the	the	DET
ajst-26903	49	10	time	time	NOUN
ajst-26903	49	11	window	window	NOUN
ajst-26903	49	12	.	.	PUNCT
ajst-26903	50	1	the	the	DET
ajst-26903	50	2	tcn	tcn	NOUN
ajst-26903	50	3	layer	layer	NOUN
ajst-26903	50	4	includes	include	VERB
ajst-26903	50	5	two	two	NUM
ajst-26903	50	6	one	one	NUM
ajst-26903	50	7	-	-	PUNCT
ajst-26903	50	8	dimensional	dimensional	ADJ
ajst-26903	50	9	convolutional	convolutional	ADJ
ajst-26903	50	10	layers	layer	NOUN
ajst-26903	50	11	,	,	PUNCT
ajst-26903	50	12	both	both	PRON
ajst-26903	50	13	utilizing	utilize	VERB
ajst-26903	50	14	dilated	dilated	ADJ
ajst-26903	50	15	convolutions	convolution	NOUN
ajst-26903	50	16	to	to	PART
ajst-26903	50	17	expand	expand	VERB
ajst-26903	50	18	the	the	DET
ajst-26903	50	19	receptive	receptive	ADJ
ajst-26903	50	20	field	field	NOUN
ajst-26903	50	21	.	.	PUNCT
ajst-26903	51	1	the	the	DET
ajst-26903	51	2	model	model	NOUN
ajst-26903	51	3	starts	start	VERB
ajst-26903	51	4	with	with	ADP
ajst-26903	51	5	10	10	NUM
ajst-26903	51	6	filters	filter	NOUN
ajst-26903	51	7	and	and	CCONJ
ajst-26903	51	8	a	a	DET
ajst-26903	51	9	3x3	3x3	NUM
ajst-26903	51	10	convolutional	convolutional	ADJ
ajst-26903	51	11	kernel	kernel	NOUN
ajst-26903	51	12	,	,	PUNCT
ajst-26903	51	13	and	and	CCONJ
ajst-26903	51	14	uses	use	VERB
ajst-26903	51	15	various	various	ADJ
ajst-26903	51	16	dilation	dilation	NOUN
ajst-26903	51	17	factors	factor	NOUN
ajst-26903	51	18	(	(	PUNCT
ajst-26903	51	19	`	`	PUNCT
ajst-26903	51	20	dilations	dilation	NOUN
ajst-26903	51	21	`	`	PUNCT
ajst-26903	51	22	)	)	PUNCT
ajst-26903	51	23	to	to	PART
ajst-26903	51	24	capture	capture	VERB
ajst-26903	51	25	patterns	pattern	NOUN
ajst-26903	51	26	at	at	ADP
ajst-26903	51	27	multiple	multiple	ADJ
ajst-26903	51	28	temporal	temporal	ADJ
ajst-26903	51	29	scales	scale	NOUN
ajst-26903	51	30	.	.	PUNCT
ajst-26903	52	1	the	the	DET
ajst-26903	52	2	output	output	NOUN
ajst-26903	52	3	layer	layer	NOUN
ajst-26903	52	4	is	be	AUX
ajst-26903	52	5	a	a	DET
ajst-26903	52	6	dense	dense	ADJ
ajst-26903	52	7	layer	layer	NOUN
ajst-26903	52	8	that	that	PRON
ajst-26903	52	9	generates	generate	VERB
ajst-26903	52	10	the	the	DET
ajst-26903	52	11	final	final	ADJ
ajst-26903	52	12	predictions	prediction	NOUN
ajst-26903	52	13	.	.	PUNCT
ajst-26903	53	1	3	3	X
ajst-26903	53	2	.	.	X
ajst-26903	53	3	compiling	compile	VERB
ajst-26903	53	4	the	the	DET
ajst-26903	53	5	model	model	NOUN
ajst-26903	53	6	:	:	PUNCT
ajst-26903	53	7	the	the	DET
ajst-26903	53	8	model	model	NOUN
ajst-26903	53	9	is	be	AUX
ajst-26903	53	10	compiled	compile	VERB
ajst-26903	53	11	using	use	VERB
ajst-26903	53	12	the	the	DET
ajst-26903	53	13	adam	adam	PROPN
ajst-26903	53	14	optimizer	optimizer	NOUN
ajst-26903	53	15	and	and	CCONJ
ajst-26903	53	16	the	the	DET
ajst-26903	53	17	mean	mean	ADJ
ajst-26903	53	18	squared	square	VERB
ajst-26903	53	19	error	error	NOUN
ajst-26903	53	20	(	(	PUNCT
ajst-26903	53	21	mse	mse	NOUN
ajst-26903	53	22	)	)	PUNCT
ajst-26903	53	23	loss	loss	NOUN
ajst-26903	53	24	function	function	NOUN
ajst-26903	53	25	.	.	PUNCT
ajst-26903	54	1	adam	adam	PROPN
ajst-26903	54	2	is	be	AUX
ajst-26903	54	3	an	an	DET
ajst-26903	54	4	adaptive	adaptive	ADJ
ajst-26903	54	5	learning	learning	NOUN
ajst-26903	54	6	rate	rate	NOUN
ajst-26903	54	7	optimization	optimization	NOUN
ajst-26903	54	8	algorithm	algorithm	NOUN
ajst-26903	54	9	that	that	PRON
ajst-26903	54	10	adjusts	adjust	VERB
ajst-26903	54	11	the	the	DET
ajst-26903	54	12	learning	learning	NOUN
ajst-26903	54	13	rate	rate	NOUN
ajst-26903	54	14	for	for	ADP
ajst-26903	54	15	each	each	DET
ajst-26903	54	16	parameter	parameter	NOUN
ajst-26903	54	17	based	base	VERB
ajst-26903	54	18	on	on	ADP
ajst-26903	54	19	the	the	DET
ajst-26903	54	20	first	first	ADJ
ajst-26903	54	21	and	and	CCONJ
ajst-26903	54	22	second	second	ADJ
ajst-26903	54	23	moments	moment	NOUN
ajst-26903	54	24	of	of	ADP
ajst-26903	54	25	the	the	DET
ajst-26903	54	26	gradients	gradient	NOUN
ajst-26903	54	27	.	.	PUNCT
ajst-26903	55	1	the	the	DET
ajst-26903	55	2	mse	mse	PROPN
ajst-26903	55	3	loss	loss	NOUN
ajst-26903	55	4	function	function	NOUN
ajst-26903	55	5	is	be	AUX
ajst-26903	55	6	chosen	choose	VERB
ajst-26903	55	7	to	to	PART
ajst-26903	55	8	measure	measure	VERB
ajst-26903	55	9	the	the	DET
ajst-26903	55	10	difference	difference	NOUN
ajst-26903	55	11	between	between	ADP
ajst-26903	55	12	predicted	predict	VERB
ajst-26903	55	13	and	and	CCONJ
ajst-26903	55	14	actual	actual	ADJ
ajst-26903	55	15	values	value	NOUN
ajst-26903	55	16	.	.	PUNCT
ajst-26903	56	1	4	4	X
ajst-26903	56	2	.	.	X
ajst-26903	56	3	training	train	VERB
ajst-26903	56	4	the	the	DET
ajst-26903	56	5	model	model	NOUN
ajst-26903	56	6	:	:	PUNCT
ajst-26903	56	7	the	the	DET
ajst-26903	56	8	model	model	NOUN
ajst-26903	56	9	is	be	AUX
ajst-26903	56	10	trained	train	VERB
ajst-26903	56	11	on	on	ADP
ajst-26903	56	12	the	the	DET
ajst-26903	56	13	training	training	NOUN
ajst-26903	56	14	set	set	VERB
ajst-26903	56	15	over	over	ADP
ajst-26903	56	16	multiple	multiple	ADJ
ajst-26903	56	17	epochs	epoch	NOUN
ajst-26903	56	18	.	.	PUNCT
ajst-26903	57	1	during	during	ADP
ajst-26903	57	2	each	each	DET
ajst-26903	57	3	epoch	epoch	NOUN
ajst-26903	57	4	,	,	PUNCT
ajst-26903	57	5	the	the	DET
ajst-26903	57	6	model	model	NOUN
ajst-26903	57	7	updates	update	VERB
ajst-26903	57	8	its	its	PRON
ajst-26903	57	9	weights	weight	NOUN
ajst-26903	57	10	to	to	PART
ajst-26903	57	11	minimize	minimize	VERB
ajst-26903	57	12	the	the	DET
ajst-26903	57	13	loss	loss	NOUN
ajst-26903	57	14	function	function	NOUN
ajst-26903	57	15	.	.	PUNCT
ajst-26903	58	1	to	to	PART
ajst-26903	58	2	monitor	monitor	VERB
ajst-26903	58	3	model	model	NOUN
ajst-26903	58	4	performance	performance	NOUN
ajst-26903	58	5	and	and	CCONJ
ajst-26903	58	6	prevent	prevent	VERB
ajst-26903	58	7	overfitting	overfitting	NOUN
ajst-26903	58	8	,	,	PUNCT
ajst-26903	58	9	20	20	NUM
ajst-26903	58	10	%	%	NOUN
ajst-26903	58	11	of	of	ADP
ajst-26903	58	12	the	the	DET
ajst-26903	58	13	training	training	NOUN
ajst-26903	58	14	data	datum	NOUN
ajst-26903	58	15	is	be	AUX
ajst-26903	58	16	used	use	VERB
ajst-26903	58	17	as	as	ADP
ajst-26903	58	18	a	a	DET
ajst-26903	58	19	validation	validation	NOUN
ajst-26903	58	20	set	set	NOUN
ajst-26903	58	21	[	[	X
ajst-26903	58	22	6	6	NUM
ajst-26903	58	23	]	]	PUNCT
ajst-26903	58	24	.	.	PUNCT
ajst-26903	59	1	3.2	3.2	NUM
ajst-26903	59	2	.	.	PUNCT
ajst-26903	59	3	model	model	NOUN
ajst-26903	59	4	parameter	parameter	NOUN
ajst-26903	59	5	based	base	VERB
ajst-26903	59	6	on	on	ADP
ajst-26903	59	7	the	the	DET
ajst-26903	59	8	grid	grid	NOUN
ajst-26903	59	9	search	search	NOUN
ajst-26903	59	10	method	method	NOUN
ajst-26903	59	11	,	,	PUNCT
ajst-26903	59	12	a	a	DET
ajst-26903	59	13	set	set	NOUN
ajst-26903	59	14	of	of	ADP
ajst-26903	59	15	parameters	parameter	NOUN
ajst-26903	59	16	with	with	ADP
ajst-26903	59	17	the	the	DET
ajst-26903	59	18	best	good	ADJ
ajst-26903	59	19	prediction	prediction	NOUN
ajst-26903	59	20	results	result	NOUN
ajst-26903	59	21	were	be	AUX
ajst-26903	59	22	finally	finally	ADV
ajst-26903	59	23	identified	identify	VERB
ajst-26903	59	24	,	,	PUNCT
ajst-26903	59	25	as	as	SCONJ
ajst-26903	59	26	shown	show	VERB
ajst-26903	59	27	in	in	ADP
ajst-26903	59	28	table	table	NOUN
ajst-26903	59	29	1	1	NUM
ajst-26903	60	1	[	[	X
ajst-26903	60	2	7	7	NUM
ajst-26903	60	3	]	]	PUNCT
ajst-26903	60	4	.	.	PUNCT
ajst-26903	61	1	table	table	NOUN
ajst-26903	61	2	1	1	NUM
ajst-26903	61	3	.	.	PUNCT
ajst-26903	61	4	model	model	NOUN
ajst-26903	61	5	setup	setup	NOUN
ajst-26903	61	6	parameters	parameter	NOUN
ajst-26903	61	7	model	model	NOUN
ajst-26903	61	8	parameter	parameter	PROPN
ajst-26903	61	9	value	value	NOUN
ajst-26903	61	10	batch_size	batch_size	VERB
ajst-26903	61	11	64	64	NUM
ajst-26903	61	12	epochs	epoch	NOUN
ajst-26903	61	13	200	200	NUM
ajst-26903	61	14	filter_nums	filter_num	NOUN
ajst-26903	61	15	10	10	NUM
ajst-26903	61	16	3.3	3.3	NUM
ajst-26903	61	17	.	.	PUNCT
ajst-26903	62	1	results	result	VERB
ajst-26903	62	2	3.3.1	3.3.1	NUM
ajst-26903	62	3	.	.	PUNCT
ajst-26903	62	4	results	result	NOUN
ajst-26903	62	5	of	of	ADP
ajst-26903	62	6	well	well	INTJ
ajst-26903	62	7	a	a	PRON
ajst-26903	62	8	in	in	ADP
ajst-26903	62	9	figure	figure	NOUN
ajst-26903	62	10	1	1	NUM
ajst-26903	62	11	,	,	PUNCT
ajst-26903	62	12	the	the	DET
ajst-26903	62	13	blue	blue	ADJ
ajst-26903	62	14	line	line	NOUN
ajst-26903	62	15	shows	show	VERB
ajst-26903	62	16	the	the	DET
ajst-26903	62	17	training	training	NOUN
ajst-26903	62	18	set	set	VERB
ajst-26903	62	19	predictions	prediction	NOUN
ajst-26903	62	20	,	,	PUNCT
ajst-26903	62	21	which	which	PRON
ajst-26903	62	22	are	be	AUX
ajst-26903	62	23	very	very	ADV
ajst-26903	62	24	close	close	ADJ
ajst-26903	62	25	to	to	ADP
ajst-26903	62	26	the	the	DET
ajst-26903	62	27	original	original	ADJ
ajst-26903	62	28	data	datum	NOUN
ajst-26903	62	29	,	,	PUNCT
ajst-26903	62	30	especially	especially	ADV
ajst-26903	62	31	in	in	ADP
ajst-26903	62	32	the	the	DET
ajst-26903	62	33	first	first	ADJ
ajst-26903	62	34	half	half	NOUN
ajst-26903	62	35	of	of	ADP
ajst-26903	62	36	the	the	DET
ajst-26903	62	37	time	time	NOUN
ajst-26903	62	38	series	series	NOUN
ajst-26903	62	39	.	.	PUNCT
ajst-26903	63	1	this	this	PRON
ajst-26903	63	2	indicates	indicate	VERB
ajst-26903	63	3	that	that	SCONJ
ajst-26903	63	4	the	the	DET
ajst-26903	63	5	training	training	NOUN
ajst-26903	63	6	model	model	NOUN
ajst-26903	63	7	captures	capture	VERB
ajst-26903	63	8	the	the	DET
ajst-26903	63	9	variation	variation	NOUN
ajst-26903	63	10	patterns	pattern	NOUN
ajst-26903	63	11	of	of	ADP
ajst-26903	63	12	the	the	DET
ajst-26903	63	13	original	original	ADJ
ajst-26903	63	14	data	datum	NOUN
ajst-26903	63	15	very	very	ADV
ajst-26903	63	16	well	well	ADV
ajst-26903	63	17	.	.	PUNCT
ajst-26903	64	1	the	the	DET
ajst-26903	64	2	green	green	ADJ
ajst-26903	64	3	line	line	NOUN
ajst-26903	64	4	is	be	AUX
ajst-26903	64	5	the	the	DET
ajst-26903	64	6	test	test	NOUN
ajst-26903	64	7	set	set	VERB
ajst-26903	64	8	yield	yield	NOUN
ajst-26903	64	9	prediction	prediction	NOUN
ajst-26903	64	10	,	,	PUNCT
ajst-26903	64	11	which	which	PRON
ajst-26903	64	12	is	be	AUX
ajst-26903	64	13	basically	basically	ADV
ajst-26903	64	14	highly	highly	ADV
ajst-26903	64	15	fitted	fit	VERB
ajst-26903	64	16	to	to	ADP
ajst-26903	64	17	the	the	DET
ajst-26903	64	18	test	test	NOUN
ajst-26903	64	19	set	set	VERB
ajst-26903	64	20	data	datum	NOUN
ajst-26903	64	21	.	.	PUNCT
ajst-26903	65	1	160	160	NUM
ajst-26903	65	2	figure	figure	NOUN
ajst-26903	65	3	1	1	NUM
ajst-26903	65	4	.	.	PUNCT
ajst-26903	66	1	well	well	INTJ
ajst-26903	66	2	a	a	DET
ajst-26903	66	3	historical	historical	ADJ
ajst-26903	66	4	forecast	forecast	NOUN
ajst-26903	66	5	zooming	zoom	VERB
ajst-26903	66	6	in	in	ADP
ajst-26903	66	7	on	on	ADP
ajst-26903	66	8	the	the	DET
ajst-26903	66	9	test	test	NOUN
ajst-26903	66	10	set	set	VERB
ajst-26903	66	11	section	section	NOUN
ajst-26903	66	12	,	,	PUNCT
ajst-26903	66	13	the	the	DET
ajst-26903	66	14	production	production	NOUN
ajst-26903	66	15	data	datum	NOUN
ajst-26903	66	16	and	and	CCONJ
ajst-26903	66	17	predicted	predict	VERB
ajst-26903	66	18	values	value	NOUN
ajst-26903	66	19	for	for	ADP
ajst-26903	66	20	the	the	DET
ajst-26903	66	21	test	test	NOUN
ajst-26903	66	22	set	set	NOUN
ajst-26903	66	23	are	be	AUX
ajst-26903	66	24	shown	show	VERB
ajst-26903	66	25	in	in	ADP
ajst-26903	66	26	figure	figure	NOUN
ajst-26903	66	27	2	2	NUM
ajst-26903	66	28	:	:	PUNCT
ajst-26903	66	29	figure	figure	NOUN
ajst-26903	66	30	2	2	NUM
ajst-26903	66	31	.	.	PUNCT
ajst-26903	67	1	well	well	INTJ
ajst-26903	67	2	a	a	DET
ajst-26903	67	3	test	test	NOUN
ajst-26903	67	4	set	set	VERB
ajst-26903	67	5	prediction	prediction	NOUN
ajst-26903	67	6	the	the	DET
ajst-26903	67	7	red	red	ADJ
ajst-26903	67	8	line	line	NOUN
ajst-26903	67	9	represents	represent	VERB
ajst-26903	67	10	the	the	DET
ajst-26903	67	11	actual	actual	ADJ
ajst-26903	67	12	data	datum	NOUN
ajst-26903	67	13	of	of	ADP
ajst-26903	67	14	the	the	DET
ajst-26903	67	15	test	test	NOUN
ajst-26903	67	16	set	set	VERB
ajst-26903	67	17	and	and	CCONJ
ajst-26903	67	18	the	the	DET
ajst-26903	67	19	green	green	ADJ
ajst-26903	67	20	line	line	NOUN
ajst-26903	67	21	represents	represent	VERB
ajst-26903	67	22	the	the	DET
ajst-26903	67	23	predicted	predict	VERB
ajst-26903	67	24	data	datum	NOUN
ajst-26903	67	25	of	of	ADP
ajst-26903	67	26	the	the	DET
ajst-26903	67	27	test	test	NOUN
ajst-26903	67	28	set	set	VERB
ajst-26903	67	29	.	.	PUNCT
ajst-26903	68	1	as	as	SCONJ
ajst-26903	68	2	can	can	AUX
ajst-26903	68	3	be	be	AUX
ajst-26903	68	4	seen	see	VERB
ajst-26903	68	5	from	from	ADP
ajst-26903	68	6	the	the	DET
ajst-26903	68	7	figure	figure	NOUN
ajst-26903	68	8	,	,	PUNCT
ajst-26903	68	9	the	the	DET
ajst-26903	68	10	two	two	NUM
ajst-26903	68	11	curves	curve	NOUN
ajst-26903	68	12	are	be	AUX
ajst-26903	68	13	very	very	ADV
ajst-26903	68	14	close	close	ADJ
ajst-26903	68	15	to	to	ADP
ajst-26903	68	16	each	each	DET
ajst-26903	68	17	other	other	ADJ
ajst-26903	68	18	most	most	ADJ
ajst-26903	68	19	of	of	ADP
ajst-26903	68	20	the	the	DET
ajst-26903	68	21	time	time	NOUN
ajst-26903	68	22	,	,	PUNCT
ajst-26903	68	23	indicating	indicate	VERB
ajst-26903	68	24	that	that	SCONJ
ajst-26903	68	25	the	the	DET
ajst-26903	68	26	predictive	predictive	ADJ
ajst-26903	68	27	model	model	NOUN
ajst-26903	68	28	fits	fit	VERB
ajst-26903	68	29	the	the	DET
ajst-26903	68	30	actual	actual	ADJ
ajst-26903	68	31	data	datum	NOUN
ajst-26903	68	32	very	very	ADV
ajst-26903	68	33	well	well	ADV
ajst-26903	68	34	.	.	PUNCT
ajst-26903	69	1	however	however	ADV
ajst-26903	69	2	,	,	PUNCT
ajst-26903	69	3	in	in	ADP
ajst-26903	69	4	some	some	DET
ajst-26903	69	5	places	place	NOUN
ajst-26903	69	6	,	,	PUNCT
ajst-26903	69	7	such	such	ADJ
ajst-26903	69	8	as	as	ADP
ajst-26903	69	9	around	around	ADP
ajst-26903	69	10	time	time	NOUN
ajst-26903	69	11	point	point	NOUN
ajst-26903	69	12	3230	3230	NUM
ajst-26903	69	13	as	as	ADV
ajst-26903	69	14	well	well	ADV
ajst-26903	69	15	as	as	ADP
ajst-26903	69	16	3620	3620	NUM
ajst-26903	69	17	,	,	PUNCT
ajst-26903	69	18	the	the	DET
ajst-26903	69	19	predicted	predict	VERB
ajst-26903	69	20	values	value	NOUN
ajst-26903	69	21	are	be	AUX
ajst-26903	69	22	slightly	slightly	ADV
ajst-26903	69	23	higher	high	ADJ
ajst-26903	69	24	than	than	ADP
ajst-26903	69	25	the	the	DET
ajst-26903	69	26	actual	actual	ADJ
ajst-26903	69	27	values	value	NOUN
ajst-26903	69	28	;	;	PUNCT
ajst-26903	69	29	while	while	SCONJ
ajst-26903	69	30	around	around	ADP
ajst-26903	69	31	time	time	NOUN
ajst-26903	69	32	point	point	NOUN
ajst-26903	69	33	3150	3150	NUM
ajst-26903	69	34	,	,	PUNCT
ajst-26903	69	35	the	the	DET
ajst-26903	69	36	predicted	predict	VERB
ajst-26903	69	37	values	value	NOUN
ajst-26903	69	38	are	be	AUX
ajst-26903	69	39	slightly	slightly	ADV
ajst-26903	69	40	lower	low	ADJ
ajst-26903	69	41	than	than	ADP
ajst-26903	69	42	the	the	DET
ajst-26903	69	43	actual	actual	ADJ
ajst-26903	69	44	values	value	NOUN
ajst-26903	69	45	.	.	PUNCT
ajst-26903	70	1	overall	overall	ADV
ajst-26903	70	2	,	,	PUNCT
ajst-26903	70	3	the	the	DET
ajst-26903	70	4	predictive	predictive	ADJ
ajst-26903	70	5	model	model	NOUN
ajst-26903	70	6	has	have	VERB
ajst-26903	70	7	a	a	DET
ajst-26903	70	8	high	high	ADJ
ajst-26903	70	9	degree	degree	NOUN
ajst-26903	70	10	of	of	ADP
ajst-26903	70	11	accuracy	accuracy	NOUN
ajst-26903	70	12	,	,	PUNCT
ajst-26903	70	13	but	but	CCONJ
ajst-26903	70	14	there	there	PRON
ajst-26903	70	15	is	be	VERB
ajst-26903	70	16	still	still	ADV
ajst-26903	70	17	room	room	NOUN
ajst-26903	70	18	for	for	ADP
ajst-26903	70	19	minor	minor	ADJ
ajst-26903	70	20	improvements	improvement	NOUN
ajst-26903	70	21	.	.	PUNCT
ajst-26903	71	1	the	the	DET
ajst-26903	71	2	test	test	NOUN
ajst-26903	71	3	set	set	VERB
ajst-26903	71	4	predictions	prediction	NOUN
ajst-26903	71	5	were	be	AUX
ajst-26903	71	6	further	far	ADV
ajst-26903	71	7	evaluated	evaluate	VERB
ajst-26903	71	8	by	by	ADP
ajst-26903	71	9	outputting	output	VERB
ajst-26903	71	10	test	test	NOUN
ajst-26903	71	11	set	set	VERB
ajst-26903	71	12	metrics	metric	NOUN
ajst-26903	71	13	,	,	PUNCT
ajst-26903	71	14	as	as	SCONJ
ajst-26903	71	15	shown	show	VERB
ajst-26903	71	16	in	in	ADP
ajst-26903	71	17	table	table	NOUN
ajst-26903	71	18	2	2	NUM
ajst-26903	71	19	.	.	PUNCT
ajst-26903	71	20	table	table	NOUN
ajst-26903	71	21	2	2	NUM
ajst-26903	71	22	.	.	PUNCT
ajst-26903	71	23	test	test	NOUN
ajst-26903	71	24	set	set	VERB
ajst-26903	71	25	output	output	NOUN
ajst-26903	71	26	metrics	metric	NOUN
ajst-26903	71	27	output	output	NOUN
ajst-26903	71	28	metrics	metric	NOUN
ajst-26903	71	29	value	value	NOUN
ajst-26903	71	30	test	test	NOUN
ajst-26903	71	31	mspe	mspe	PROPN
ajst-26903	71	32	0.0109	0.0109	NUM
ajst-26903	71	33	test	test	NOUN
ajst-26903	71	34	mae	mae	PROPN
ajst-26903	71	35	0.0022	0.0022	NUM
ajst-26903	71	36	test	test	NOUN
ajst-26903	71	37	rmse	rmse	NOUN
ajst-26903	71	38	0.0034	0.0034	NUM
ajst-26903	71	39	r2	r2	NOUN
ajst-26903	71	40	0.9817	0.9817	NUM
ajst-26903	71	41	the	the	DET
ajst-26903	71	42	mspe	mspe	PROPN
ajst-26903	71	43	,	,	PUNCT
ajst-26903	71	44	mae	mae	PROPN
ajst-26903	71	45	,	,	PUNCT
ajst-26903	71	46	and	and	CCONJ
ajst-26903	71	47	rmse	rmse	ADJ
ajst-26903	71	48	values	value	NOUN
ajst-26903	71	49	are	be	AUX
ajst-26903	71	50	very	very	ADV
ajst-26903	71	51	small	small	ADJ
ajst-26903	71	52	,	,	PUNCT
ajst-26903	71	53	indicating	indicate	VERB
ajst-26903	71	54	that	that	SCONJ
ajst-26903	71	55	the	the	DET
ajst-26903	71	56	model	model	NOUN
ajst-26903	71	57	demonstrates	demonstrate	VERB
ajst-26903	71	58	high	high	ADJ
ajst-26903	71	59	prediction	prediction	NOUN
ajst-26903	71	60	accuracy	accuracy	NOUN
ajst-26903	71	61	.	.	PUNCT
ajst-26903	72	1	additionally	additionally	ADV
ajst-26903	72	2	,	,	PUNCT
ajst-26903	72	3	the	the	DET
ajst-26903	72	4	r2	r2	PROPN
ajst-26903	72	5	value	value	NOUN
ajst-26903	72	6	is	be	AUX
ajst-26903	72	7	close	close	ADJ
ajst-26903	72	8	to	to	ADP
ajst-26903	72	9	1	1	NUM
ajst-26903	72	10	,	,	PUNCT
ajst-26903	72	11	signifying	signify	VERB
ajst-26903	72	12	a	a	DET
ajst-26903	72	13	good	good	ADJ
ajst-26903	72	14	fit	fit	NOUN
ajst-26903	72	15	to	to	ADP
ajst-26903	72	16	the	the	DET
ajst-26903	72	17	data	datum	NOUN
ajst-26903	72	18	,	,	PUNCT
ajst-26903	72	19	with	with	ADP
ajst-26903	72	20	only	only	ADV
ajst-26903	72	21	about	about	ADV
ajst-26903	72	22	1.8	1.8	NUM
ajst-26903	72	23	%	%	NOUN
ajst-26903	72	24	of	of	ADP
ajst-26903	72	25	the	the	DET
ajst-26903	72	26	variance	variance	NOUN
ajst-26903	72	27	in	in	ADP
ajst-26903	72	28	the	the	DET
ajst-26903	72	29	data	datum	NOUN
ajst-26903	72	30	not	not	PART
ajst-26903	72	31	explained	explain	VERB
ajst-26903	72	32	by	by	ADP
ajst-26903	72	33	the	the	DET
ajst-26903	72	34	model	model	NOUN
ajst-26903	72	35	.	.	PUNCT
ajst-26903	73	1	consequently	consequently	ADV
ajst-26903	73	2	,	,	PUNCT
ajst-26903	73	3	the	the	DET
ajst-26903	73	4	tcn	tcn	NOUN
ajst-26903	73	5	161	161	NUM
ajst-26903	73	6	model	model	NOUN
ajst-26903	73	7	is	be	AUX
ajst-26903	73	8	highly	highly	ADV
ajst-26903	73	9	effective	effective	ADJ
ajst-26903	73	10	in	in	ADP
ajst-26903	73	11	predicting	predict	VERB
ajst-26903	73	12	daily	daily	ADJ
ajst-26903	73	13	gas	gas	NOUN
ajst-26903	73	14	production	production	NOUN
ajst-26903	73	15	from	from	ADP
ajst-26903	73	16	well	well	ADV
ajst-26903	73	17	a.	a.	NOUN
ajst-26903	73	18	3.3.2	3.3.2	NUM
ajst-26903	73	19	.	.	PUNCT
ajst-26903	74	1	results	result	NOUN
ajst-26903	74	2	of	of	ADP
ajst-26903	74	3	well	well	ADV
ajst-26903	74	4	b	b	NOUN
ajst-26903	74	5	in	in	ADP
ajst-26903	74	6	figure	figure	NOUN
ajst-26903	74	7	3	3	NUM
ajst-26903	74	8	,	,	PUNCT
ajst-26903	74	9	the	the	DET
ajst-26903	74	10	blue	blue	ADJ
ajst-26903	74	11	line	line	NOUN
ajst-26903	74	12	illustrates	illustrate	VERB
ajst-26903	74	13	the	the	DET
ajst-26903	74	14	predictions	prediction	NOUN
ajst-26903	74	15	for	for	ADP
ajst-26903	74	16	the	the	DET
ajst-26903	74	17	training	training	NOUN
ajst-26903	74	18	set	set	NOUN
ajst-26903	74	19	,	,	PUNCT
ajst-26903	74	20	the	the	DET
ajst-26903	74	21	green	green	ADJ
ajst-26903	74	22	line	line	NOUN
ajst-26903	74	23	represents	represent	VERB
ajst-26903	74	24	the	the	DET
ajst-26903	74	25	predictions	prediction	NOUN
ajst-26903	74	26	for	for	ADP
ajst-26903	74	27	the	the	DET
ajst-26903	74	28	test	test	NOUN
ajst-26903	74	29	set	set	NOUN
ajst-26903	74	30	,	,	PUNCT
ajst-26903	74	31	and	and	CCONJ
ajst-26903	74	32	the	the	DET
ajst-26903	74	33	red	red	ADJ
ajst-26903	74	34	line	line	NOUN
ajst-26903	74	35	depicts	depict	VERB
ajst-26903	74	36	the	the	DET
ajst-26903	74	37	complete	complete	ADJ
ajst-26903	74	38	historical	historical	ADJ
ajst-26903	74	39	production	production	NOUN
ajst-26903	74	40	data	datum	NOUN
ajst-26903	74	41	.	.	PUNCT
ajst-26903	75	1	the	the	DET
ajst-26903	75	2	red	red	ADJ
ajst-26903	75	3	line	line	NOUN
ajst-26903	75	4	is	be	AUX
ajst-26903	75	5	nearly	nearly	ADV
ajst-26903	75	6	invisible	invisible	ADJ
ajst-26903	75	7	in	in	ADP
ajst-26903	75	8	the	the	DET
ajst-26903	75	9	figure	figure	NOUN
ajst-26903	75	10	,	,	PUNCT
ajst-26903	75	11	suggesting	suggest	VERB
ajst-26903	75	12	that	that	SCONJ
ajst-26903	75	13	the	the	DET
ajst-26903	75	14	trained	train	VERB
ajst-26903	75	15	model	model	NOUN
ajst-26903	75	16	performs	perform	VERB
ajst-26903	75	17	exceptionally	exceptionally	ADV
ajst-26903	75	18	well	well	ADV
ajst-26903	75	19	in	in	ADP
ajst-26903	75	20	predicting	predict	VERB
ajst-26903	75	21	gas	gas	NOUN
ajst-26903	75	22	well	well	NOUN
ajst-26903	75	23	production	production	NOUN
ajst-26903	75	24	.	.	PUNCT
ajst-26903	76	1	figure	figure	NOUN
ajst-26903	76	2	3	3	NUM
ajst-26903	76	3	.	.	PUNCT
ajst-26903	77	1	well	well	INTJ
ajst-26903	77	2	b	b	ADP
ajst-26903	77	3	historical	historical	ADJ
ajst-26903	77	4	forecast	forecast	NOUN
ajst-26903	77	5	focusing	focus	VERB
ajst-26903	77	6	on	on	ADP
ajst-26903	77	7	the	the	DET
ajst-26903	77	8	test	test	NOUN
ajst-26903	77	9	set	set	NOUN
ajst-26903	77	10	,	,	PUNCT
ajst-26903	77	11	figure	figure	NOUN
ajst-26903	77	12	4	4	NUM
ajst-26903	77	13	provides	provide	VERB
ajst-26903	77	14	a	a	DET
ajst-26903	77	15	closer	close	ADJ
ajst-26903	77	16	look	look	NOUN
ajst-26903	77	17	at	at	ADP
ajst-26903	77	18	the	the	DET
ajst-26903	77	19	test	test	NOUN
ajst-26903	77	20	set	set	VERB
ajst-26903	77	21	data	datum	NOUN
ajst-26903	77	22	alongside	alongside	ADP
ajst-26903	77	23	the	the	DET
ajst-26903	77	24	corresponding	corresponding	ADJ
ajst-26903	77	25	prediction	prediction	NOUN
ajst-26903	77	26	data	datum	NOUN
ajst-26903	77	27	.	.	PUNCT
ajst-26903	78	1	this	this	DET
ajst-26903	78	2	zoomed	zoomed	PROPN
ajst-26903	78	3	-	-	PUNCT
ajst-26903	78	4	in	in	ADP
ajst-26903	78	5	view	view	NOUN
ajst-26903	78	6	allows	allow	VERB
ajst-26903	78	7	for	for	ADP
ajst-26903	78	8	a	a	DET
ajst-26903	78	9	more	more	ADV
ajst-26903	78	10	detailed	detailed	ADJ
ajst-26903	78	11	comparison	comparison	NOUN
ajst-26903	78	12	between	between	ADP
ajst-26903	78	13	the	the	DET
ajst-26903	78	14	actual	actual	ADJ
ajst-26903	78	15	production	production	NOUN
ajst-26903	78	16	values	value	NOUN
ajst-26903	78	17	and	and	CCONJ
ajst-26903	78	18	the	the	DET
ajst-26903	78	19	model	model	NOUN
ajst-26903	78	20	's	's	PART
ajst-26903	78	21	predictions	prediction	NOUN
ajst-26903	78	22	for	for	ADP
ajst-26903	78	23	the	the	DET
ajst-26903	78	24	test	test	NOUN
ajst-26903	78	25	set	set	NOUN
ajst-26903	78	26	,	,	PUNCT
ajst-26903	78	27	further	far	ADV
ajst-26903	78	28	demonstrating	demonstrate	VERB
ajst-26903	78	29	the	the	DET
ajst-26903	78	30	model	model	NOUN
ajst-26903	78	31	's	's	PART
ajst-26903	78	32	effectiveness	effectiveness	NOUN
ajst-26903	78	33	.	.	PUNCT
ajst-26903	79	1	figure	figure	NOUN
ajst-26903	79	2	4	4	NUM
ajst-26903	79	3	.	.	PUNCT
ajst-26903	80	1	well	well	INTJ
ajst-26903	80	2	b	b	X
ajst-26903	80	3	test	test	NOUN
ajst-26903	80	4	set	set	VERB
ajst-26903	80	5	prediction	prediction	NOUN
ajst-26903	80	6	the	the	DET
ajst-26903	80	7	graph	graph	NOUN
ajst-26903	80	8	clearly	clearly	ADV
ajst-26903	80	9	shows	show	VERB
ajst-26903	80	10	that	that	SCONJ
ajst-26903	80	11	the	the	DET
ajst-26903	80	12	predicted	predict	VERB
ajst-26903	80	13	and	and	CCONJ
ajst-26903	80	14	actual	actual	ADJ
ajst-26903	80	15	values	value	NOUN
ajst-26903	80	16	closely	closely	ADV
ajst-26903	80	17	align	align	VERB
ajst-26903	80	18	for	for	ADP
ajst-26903	80	19	most	most	ADJ
ajst-26903	80	20	of	of	ADP
ajst-26903	80	21	the	the	DET
ajst-26903	80	22	observed	observed	ADJ
ajst-26903	80	23	period	period	NOUN
ajst-26903	80	24	,	,	PUNCT
ajst-26903	80	25	indicating	indicate	VERB
ajst-26903	80	26	a	a	DET
ajst-26903	80	27	very	very	ADV
ajst-26903	80	28	high	high	ADJ
ajst-26903	80	29	degree	degree	NOUN
ajst-26903	80	30	of	of	ADP
ajst-26903	80	31	agreement	agreement	NOUN
ajst-26903	80	32	.	.	PUNCT
ajst-26903	81	1	notably	notably	ADV
ajst-26903	81	2	,	,	PUNCT
ajst-26903	81	3	between	between	ADP
ajst-26903	81	4	the	the	DET
ajst-26903	81	5	time	time	NOUN
ajst-26903	81	6	periods	period	NOUN
ajst-26903	81	7	of	of	ADP
ajst-26903	81	8	1600	1600	NUM
ajst-26903	81	9	and	and	CCONJ
ajst-26903	81	10	1700	1700	NUM
ajst-26903	81	11	,	,	PUNCT
ajst-26903	81	12	the	the	DET
ajst-26903	81	13	predicted	predict	VERB
ajst-26903	81	14	values	value	NOUN
ajst-26903	81	15	closely	closely	ADV
ajst-26903	81	16	track	track	VERB
ajst-26903	81	17	the	the	DET
ajst-26903	81	18	actual	actual	ADJ
ajst-26903	81	19	values	value	NOUN
ajst-26903	81	20	,	,	PUNCT
ajst-26903	81	21	demonstrating	demonstrate	VERB
ajst-26903	81	22	exceptional	exceptional	ADJ
ajst-26903	81	23	accuracy	accuracy	NOUN
ajst-26903	81	24	.	.	PUNCT
ajst-26903	82	1	however	however	ADV
ajst-26903	82	2	,	,	PUNCT
ajst-26903	82	3	around	around	ADP
ajst-26903	82	4	the	the	DET
ajst-26903	82	5	time	time	NOUN
ajst-26903	82	6	point	point	NOUN
ajst-26903	82	7	of	of	ADP
ajst-26903	82	8	2000	2000	NUM
ajst-26903	82	9	,	,	PUNCT
ajst-26903	82	10	the	the	DET
ajst-26903	82	11	predicted	predict	VERB
ajst-26903	82	12	line	line	NOUN
ajst-26903	82	13	dips	dip	VERB
ajst-26903	82	14	slightly	slightly	ADV
ajst-26903	82	15	below	below	ADP
ajst-26903	82	16	the	the	DET
ajst-26903	82	17	actual	actual	ADJ
ajst-26903	82	18	line	line	NOUN
ajst-26903	82	19	,	,	PUNCT
ajst-26903	82	20	suggesting	suggest	VERB
ajst-26903	82	21	that	that	SCONJ
ajst-26903	82	22	the	the	DET
ajst-26903	82	23	model	model	NOUN
ajst-26903	82	24	may	may	AUX
ajst-26903	82	25	be	be	AUX
ajst-26903	82	26	overestimating	overestimate	VERB
ajst-26903	82	27	future	future	ADJ
ajst-26903	82	28	values	value	NOUN
ajst-26903	82	29	at	at	ADP
ajst-26903	82	30	this	this	DET
ajst-26903	82	31	stage	stage	NOUN
ajst-26903	82	32	.	.	PUNCT
ajst-26903	83	1	overall	overall	ADV
ajst-26903	83	2	,	,	PUNCT
ajst-26903	83	3	the	the	DET
ajst-26903	83	4	model	model	NOUN
ajst-26903	83	5	exhibits	exhibit	VERB
ajst-26903	83	6	impressive	impressive	ADJ
ajst-26903	83	7	accuracy	accuracy	NOUN
ajst-26903	83	8	in	in	ADP
ajst-26903	83	9	predicting	predict	VERB
ajst-26903	83	10	gas	gas	NOUN
ajst-26903	83	11	well	well	NOUN
ajst-26903	83	12	production	production	NOUN
ajst-26903	83	13	.	.	PUNCT
ajst-26903	84	1	to	to	PART
ajst-26903	84	2	provide	provide	VERB
ajst-26903	84	3	a	a	DET
ajst-26903	84	4	more	more	ADV
ajst-26903	84	5	comprehensive	comprehensive	ADJ
ajst-26903	84	6	evaluation	evaluation	NOUN
ajst-26903	84	7	,	,	PUNCT
ajst-26903	84	8	the	the	DET
ajst-26903	84	9	performance	performance	NOUN
ajst-26903	84	10	of	of	ADP
ajst-26903	84	11	the	the	DET
ajst-26903	84	12	model	model	NOUN
ajst-26903	84	13	on	on	ADP
ajst-26903	84	14	the	the	DET
ajst-26903	84	15	test	test	NOUN
ajst-26903	84	16	set	set	NOUN
ajst-26903	84	17	is	be	AUX
ajst-26903	84	18	further	far	ADV
ajst-26903	84	19	assessed	assess	VERB
ajst-26903	84	20	through	through	ADP
ajst-26903	84	21	various	various	ADJ
ajst-26903	84	22	metrics	metric	NOUN
ajst-26903	84	23	,	,	PUNCT
ajst-26903	84	24	which	which	PRON
ajst-26903	84	25	are	be	AUX
ajst-26903	84	26	presented	present	VERB
ajst-26903	84	27	in	in	ADP
ajst-26903	84	28	table	table	NOUN
ajst-26903	84	29	3	3	NUM
ajst-26903	84	30	.	.	NOUN
ajst-26903	84	31	162	162	NUM
ajst-26903	84	32	table	table	NOUN
ajst-26903	84	33	3	3	NUM
ajst-26903	84	34	.	.	PUNCT
ajst-26903	84	35	test	test	NOUN
ajst-26903	84	36	set	set	VERB
ajst-26903	84	37	output	output	NOUN
ajst-26903	84	38	metrics	metric	NOUN
ajst-26903	84	39	output	output	NOUN
ajst-26903	84	40	metrics	metric	NOUN
ajst-26903	84	41	value	value	NOUN
ajst-26903	84	42	test	test	NOUN
ajst-26903	84	43	mse	mse	NOUN
ajst-26903	84	44	0.0389	0.0389	NUM
ajst-26903	84	45	test	test	NOUN
ajst-26903	84	46	mae	mae	PROPN
ajst-26903	84	47	0.0047	0.0047	NUM
ajst-26903	84	48	test	test	NOUN
ajst-26903	84	49	rmse	rmse	NOUN
ajst-26903	84	50	0.0076	0.0076	NUM
ajst-26903	84	51	r2	r2	PROPN
ajst-26903	84	52	0.9915	0.9915	NUM
ajst-26903	84	53	the	the	DET
ajst-26903	84	54	values	value	NOUN
ajst-26903	84	55	for	for	ADP
ajst-26903	84	56	mse	mse	PROPN
ajst-26903	84	57	,	,	PUNCT
ajst-26903	84	58	mae	mae	PROPN
ajst-26903	84	59	,	,	PUNCT
ajst-26903	84	60	and	and	CCONJ
ajst-26903	84	61	rmse	rmse	NOUN
ajst-26903	84	62	are	be	AUX
ajst-26903	84	63	all	all	ADV
ajst-26903	84	64	close	close	ADJ
ajst-26903	84	65	to	to	ADP
ajst-26903	84	66	zero	zero	NUM
ajst-26903	84	67	,	,	PUNCT
ajst-26903	84	68	indicating	indicate	VERB
ajst-26903	84	69	that	that	SCONJ
ajst-26903	84	70	the	the	DET
ajst-26903	84	71	deviation	deviation	NOUN
ajst-26903	84	72	between	between	ADP
ajst-26903	84	73	the	the	DET
ajst-26903	84	74	predicted	predict	VERB
ajst-26903	84	75	and	and	CCONJ
ajst-26903	84	76	actual	actual	ADJ
ajst-26903	84	77	values	value	NOUN
ajst-26903	84	78	is	be	AUX
ajst-26903	84	79	extremely	extremely	ADV
ajst-26903	84	80	small	small	ADJ
ajst-26903	84	81	.	.	PUNCT
ajst-26903	85	1	this	this	PRON
ajst-26903	85	2	reflects	reflect	VERB
ajst-26903	85	3	the	the	DET
ajst-26903	85	4	model	model	NOUN
ajst-26903	85	5	's	's	PART
ajst-26903	85	6	very	very	ADV
ajst-26903	85	7	high	high	ADJ
ajst-26903	85	8	prediction	prediction	NOUN
ajst-26903	85	9	accuracy	accuracy	NOUN
ajst-26903	85	10	.	.	PUNCT
ajst-26903	86	1	additionally	additionally	ADV
ajst-26903	86	2	,	,	PUNCT
ajst-26903	86	3	the	the	DET
ajst-26903	86	4	coefficient	coefficient	NOUN
ajst-26903	86	5	of	of	ADP
ajst-26903	86	6	determination	determination	NOUN
ajst-26903	86	7	r2	r2	PROPN
ajst-26903	86	8	is	be	AUX
ajst-26903	86	9	notably	notably	ADV
ajst-26903	86	10	high	high	ADJ
ajst-26903	86	11	at	at	ADP
ajst-26903	86	12	0.9915	0.9915	NUM
ajst-26903	86	13	,	,	PUNCT
ajst-26903	86	14	demonstrating	demonstrate	VERB
ajst-26903	86	15	that	that	SCONJ
ajst-26903	86	16	the	the	DET
ajst-26903	86	17	model	model	NOUN
ajst-26903	86	18	explains	explain	VERB
ajst-26903	86	19	over	over	ADP
ajst-26903	86	20	99.15	99.15	NUM
ajst-26903	86	21	%	%	NOUN
ajst-26903	86	22	of	of	ADP
ajst-26903	86	23	the	the	DET
ajst-26903	86	24	variability	variability	NOUN
ajst-26903	86	25	in	in	ADP
ajst-26903	86	26	the	the	DET
ajst-26903	86	27	data	datum	NOUN
ajst-26903	86	28	.	.	PUNCT
ajst-26903	87	1	this	this	PRON
ajst-26903	87	2	indicates	indicate	VERB
ajst-26903	87	3	an	an	DET
ajst-26903	87	4	excellent	excellent	ADJ
ajst-26903	87	5	fit	fit	NOUN
ajst-26903	87	6	,	,	PUNCT
ajst-26903	87	7	further	far	ADV
ajst-26903	87	8	reinforcing	reinforce	VERB
ajst-26903	87	9	the	the	DET
ajst-26903	87	10	model	model	NOUN
ajst-26903	87	11	's	's	PART
ajst-26903	87	12	effectiveness	effectiveness	NOUN
ajst-26903	87	13	in	in	ADP
ajst-26903	87	14	predicting	predict	VERB
ajst-26903	87	15	gas	gas	NOUN
ajst-26903	87	16	well	well	NOUN
ajst-26903	87	17	production	production	NOUN
ajst-26903	87	18	.	.	PUNCT
ajst-26903	88	1	4	4	X
ajst-26903	88	2	.	.	X
ajst-26903	88	3	conclusion	conclusion	NOUN
ajst-26903	88	4	this	this	DET
ajst-26903	88	5	paper	paper	NOUN
ajst-26903	88	6	examines	examine	VERB
ajst-26903	88	7	the	the	DET
ajst-26903	88	8	gas	gas	NOUN
ajst-26903	88	9	well	well	NOUN
ajst-26903	88	10	production	production	NOUN
ajst-26903	88	11	prediction	prediction	NOUN
ajst-26903	88	12	method	method	NOUN
ajst-26903	88	13	using	use	VERB
ajst-26903	88	14	temporal	temporal	ADJ
ajst-26903	88	15	convolutional	convolutional	ADJ
ajst-26903	88	16	networks	network	NOUN
ajst-26903	88	17	(	(	PUNCT
ajst-26903	88	18	tcn	tcn	PROPN
ajst-26903	88	19	)	)	PUNCT
ajst-26903	88	20	and	and	CCONJ
ajst-26903	88	21	highlights	highlight	VERB
ajst-26903	88	22	the	the	DET
ajst-26903	88	23	advantages	advantage	NOUN
ajst-26903	88	24	of	of	ADP
ajst-26903	88	25	tcns	tcns	NOUN
ajst-26903	88	26	in	in	ADP
ajst-26903	88	27	improving	improve	VERB
ajst-26903	88	28	prediction	prediction	NOUN
ajst-26903	88	29	accuracy	accuracy	NOUN
ajst-26903	88	30	.	.	PUNCT
ajst-26903	89	1	a	a	DET
ajst-26903	89	2	tcn	tcn	NOUN
ajst-26903	89	3	model	model	NOUN
ajst-26903	89	4	was	be	AUX
ajst-26903	89	5	constructed	construct	VERB
ajst-26903	89	6	for	for	ADP
ajst-26903	89	7	two	two	NUM
ajst-26903	89	8	gas	gas	NOUN
ajst-26903	89	9	wells	well	NOUN
ajst-26903	89	10	,	,	PUNCT
ajst-26903	89	11	utilizing	utilize	VERB
ajst-26903	89	12	the	the	DET
ajst-26903	89	13	same	same	ADJ
ajst-26903	89	14	production	production	NOUN
ajst-26903	89	15	data	datum	NOUN
ajst-26903	89	16	for	for	ADP
ajst-26903	89	17	predictions	prediction	NOUN
ajst-26903	89	18	.	.	PUNCT
ajst-26903	90	1	the	the	DET
ajst-26903	90	2	model	model	NOUN
ajst-26903	90	3	's	's	PART
ajst-26903	90	4	performance	performance	NOUN
ajst-26903	90	5	was	be	AUX
ajst-26903	90	6	evaluated	evaluate	VERB
ajst-26903	90	7	using	use	VERB
ajst-26903	90	8	four	four	NUM
ajst-26903	90	9	metrics	metric	NOUN
ajst-26903	90	10	:	:	PUNCT
ajst-26903	90	11	mse	mse	PROPN
ajst-26903	90	12	,	,	PUNCT
ajst-26903	90	13	mae	mae	PROPN
ajst-26903	90	14	,	,	PUNCT
ajst-26903	90	15	rmse	rmse	NOUN
ajst-26903	90	16	,	,	PUNCT
ajst-26903	90	17	and	and	CCONJ
ajst-26903	90	18	\	\	PROPN
ajst-26903	90	19	(	(	PUNCT
ajst-26903	90	20	r^2	r^2	PROPN
ajst-26903	90	21	\	\	PROPN
ajst-26903	90	22	)	)	PUNCT
ajst-26903	90	23	.	.	PUNCT
ajst-26903	91	1	the	the	DET
ajst-26903	91	2	results	result	NOUN
ajst-26903	91	3	demonstrate	demonstrate	VERB
ajst-26903	91	4	that	that	SCONJ
ajst-26903	91	5	the	the	DET
ajst-26903	91	6	tcn	tcn	NOUN
ajst-26903	91	7	method	method	NOUN
ajst-26903	91	8	yields	yield	VERB
ajst-26903	91	9	lower	low	ADJ
ajst-26903	91	10	values	value	NOUN
ajst-26903	91	11	for	for	ADP
ajst-26903	91	12	mse	mse	PROPN
ajst-26903	91	13	,	,	PUNCT
ajst-26903	91	14	mae	mae	PROPN
ajst-26903	91	15	,	,	PUNCT
ajst-26903	91	16	and	and	CCONJ
ajst-26903	91	17	rmse	rmse	NOUN
ajst-26903	91	18	on	on	ADP
ajst-26903	91	19	the	the	DET
ajst-26903	91	20	test	test	NOUN
ajst-26903	91	21	set	set	NOUN
ajst-26903	91	22	,	,	PUNCT
ajst-26903	91	23	indicating	indicate	VERB
ajst-26903	91	24	superior	superior	ADJ
ajst-26903	91	25	prediction	prediction	NOUN
ajst-26903	91	26	accuracy	accuracy	NOUN
ajst-26903	91	27	and	and	CCONJ
ajst-26903	91	28	model	model	NOUN
ajst-26903	91	29	stability	stability	NOUN
ajst-26903	91	30	.	.	PUNCT
ajst-26903	92	1	overall	overall	ADV
ajst-26903	92	2	,	,	PUNCT
ajst-26903	92	3	the	the	DET
ajst-26903	92	4	tcn	tcn	NOUN
ajst-26903	92	5	method	method	NOUN
ajst-26903	92	6	presents	present	VERB
ajst-26903	92	7	a	a	DET
ajst-26903	92	8	novel	novel	ADJ
ajst-26903	92	9	approach	approach	NOUN
ajst-26903	92	10	for	for	ADP
ajst-26903	92	11	gas	gas	NOUN
ajst-26903	92	12	well	well	NOUN
ajst-26903	92	13	production	production	NOUN
ajst-26903	92	14	prediction	prediction	NOUN
ajst-26903	92	15	,	,	PUNCT
ajst-26903	92	16	showcasing	showcase	VERB
ajst-26903	92	17	significant	significant	ADJ
ajst-26903	92	18	potential	potential	NOUN
ajst-26903	92	19	for	for	ADP
ajst-26903	92	20	widespread	widespread	ADJ
ajst-26903	92	21	application	application	NOUN
ajst-26903	92	22	.	.	PUNCT
ajst-26903	93	1	references	reference	NOUN
ajst-26903	93	2	[	[	X
ajst-26903	93	3	1	1	NUM
ajst-26903	93	4	]	]	X
ajst-26903	93	5	arps	arps	PROPN
ajst-26903	93	6	,	,	PUNCT
ajst-26903	93	7	j.j	j.j	PROPN
ajst-26903	93	8	.	.	PROPN
ajst-26903	93	9	analysis	analysis	NOUN
ajst-26903	93	10	of	of	ADP
ajst-26903	93	11	decline	decline	NOUN
ajst-26903	93	12	curves	curve	NOUN
ajst-26903	93	13	.	.	PUNCT
ajst-26903	94	1	trans	tran	NOUN
ajst-26903	94	2	.	.	PUNCT
ajst-26903	94	3	aime	aime	PROPN
ajst-26903	94	4	1945	1945	NUM
ajst-26903	94	5	,	,	PUNCT
ajst-26903	94	6	160	160	NUM
ajst-26903	94	7	,	,	PUNCT
ajst-26903	94	8	228–247	228–247	NUM
ajst-26903	94	9	.	.	PUNCT
ajst-26903	95	1	[	[	X
ajst-26903	95	2	2	2	NUM
ajst-26903	95	3	]	]	X
ajst-26903	95	4	song	song	NOUN
ajst-26903	95	5	,	,	PUNCT
ajst-26903	95	6	x.	x.	PROPN
ajst-26903	95	7	;	;	PUNCT
ajst-26903	95	8	liu	liu	PROPN
ajst-26903	95	9	,	,	PUNCT
ajst-26903	95	10	y.	y.	PROPN
ajst-26903	95	11	;	;	PUNCT
ajst-26903	95	12	xue	xue	PROPN
ajst-26903	95	13	,	,	PUNCT
ajst-26903	95	14	l.	l.	PROPN
ajst-26903	95	15	;	;	PUNCT
ajst-26903	95	16	wang	wang	PROPN
ajst-26903	95	17	,	,	PUNCT
ajst-26903	95	18	j.	j.	PROPN
ajst-26903	95	19	;	;	PUNCT
ajst-26903	95	20	zhang	zhang	PROPN
ajst-26903	95	21	,	,	PUNCT
ajst-26903	95	22	j.	j.	PROPN
ajst-26903	95	23	;	;	PUNCT
ajst-26903	95	24	wang	wang	PROPN
ajst-26903	95	25	,	,	PUNCT
ajst-26903	95	26	j.	j.	PROPN
ajst-26903	95	27	;	;	PUNCT
ajst-26903	95	28	jiang	jiang	PROPN
ajst-26903	95	29	,	,	PUNCT
ajst-26903	95	30	l.	l.	PROPN
ajst-26903	95	31	;	;	PUNCT
ajst-26903	95	32	cheng	cheng	PROPN
ajst-26903	95	33	,	,	PUNCT
ajst-26903	95	34	z.	z.	PROPN
ajst-26903	95	35	time	time	PROPN
ajst-26903	95	36	-	-	PUNCT
ajst-26903	95	37	series	series	NOUN
ajst-26903	95	38	well	well	ADJ
ajst-26903	95	39	performance	performance	NOUN
ajst-26903	95	40	prediction	prediction	NOUN
ajst-26903	95	41	based	base	VERB
ajst-26903	95	42	on	on	ADP
ajst-26903	95	43	long	long	ADJ
ajst-26903	95	44	short	short	ADJ
ajst-26903	95	45	-	-	PUNCT
ajst-26903	95	46	term	term	NOUN
ajst-26903	95	47	memory	memory	NOUN
ajst-26903	95	48	(	(	PUNCT
ajst-26903	95	49	lstm	lstm	ADJ
ajst-26903	95	50	)	)	PUNCT
ajst-26903	95	51	neural	neural	ADJ
ajst-26903	95	52	network	network	NOUN
ajst-26903	95	53	model	model	NOUN
ajst-26903	95	54	.	.	PUNCT
ajst-26903	96	1	j.	j.	PROPN
ajst-26903	96	2	pet	pet	PROPN
ajst-26903	96	3	.	.	PUNCT
ajst-26903	97	1	sci	sci	PROPN
ajst-26903	97	2	.	.	PUNCT
ajst-26903	98	1	eng	eng	PROPN
ajst-26903	98	2	.	.	PROPN
ajst-26903	98	3	2020	2020	NUM
ajst-26903	98	4	,	,	PUNCT
ajst-26903	98	5	186	186	NUM
ajst-26903	98	6	,	,	PUNCT
ajst-26903	98	7	106682	106682	NUM
ajst-26903	98	8	.	.	PUNCT
ajst-26903	99	1	[	[	X
ajst-26903	99	2	3	3	NUM
ajst-26903	99	3	]	]	X
ajst-26903	99	4	qiao	qiao	NOUN
ajst-26903	99	5	,	,	PUNCT
ajst-26903	99	6	x.	x.	NOUN
ajst-26903	99	7	status	status	NOUN
ajst-26903	99	8	analysis	analysis	NOUN
ajst-26903	99	9	and	and	CCONJ
ajst-26903	99	10	development	development	NOUN
ajst-26903	99	11	of	of	ADP
ajst-26903	99	12	reservoir	reservoir	PROPN
ajst-26903	99	13	numerical	numerical	PROPN
ajst-26903	99	14	simulation	simulation	PROPN
ajst-26903	99	15	technology	technology	PROPN
ajst-26903	99	16	.	.	PUNCT
ajst-26903	100	1	iop	iop	PROPN
ajst-26903	100	2	conf	conf	PROPN
ajst-26903	100	3	.	.	PUNCT
ajst-26903	101	1	ser	ser	PROPN
ajst-26903	101	2	.	.	PROPN
ajst-26903	101	3	earth	earth	PROPN
ajst-26903	101	4	environ	environ	PROPN
ajst-26903	101	5	.	.	PUNCT
ajst-26903	102	1	sci	sci	PROPN
ajst-26903	102	2	.	.	PROPN
ajst-26903	102	3	2021	2021	NUM
ajst-26903	102	4	,	,	PUNCT
ajst-26903	102	5	631	631	NUM
ajst-26903	102	6	,	,	PUNCT
ajst-26903	102	7	012052	012052	NUM
ajst-26903	102	8	.	.	PUNCT
ajst-26903	103	1	[	[	X
ajst-26903	103	2	4	4	NUM
ajst-26903	103	3	]	]	X
ajst-26903	103	4	zhen	zhen	PROPN
ajst-26903	103	5	,	,	PUNCT
ajst-26903	103	6	y.	y.	PROPN
ajst-26903	103	7	;	;	PUNCT
ajst-26903	103	8	fang	fang	X
ajst-26903	103	9	,	,	PUNCT
ajst-26903	103	10	j.	j.	PROPN
ajst-26903	103	11	;	;	PUNCT
ajst-26903	103	12	zhao	zhao	PROPN
ajst-26903	103	13	,	,	PUNCT
ajst-26903	103	14	x.	x.	PROPN
ajst-26903	103	15	;	;	PUNCT
ajst-26903	103	16	ge	ge	PROPN
ajst-26903	103	17	,	,	PUNCT
ajst-26903	103	18	j.	j.	PROPN
ajst-26903	103	19	;	;	PUNCT
ajst-26903	103	20	xiao	xiao	PROPN
ajst-26903	103	21	,	,	PUNCT
ajst-26903	103	22	y.	y.	PROPN
ajst-26903	103	23	temporal	temporal	ADJ
ajst-26903	103	24	convolution	convolution	NOUN
ajst-26903	103	25	network	network	NOUN
ajst-26903	103	26	based	base	VERB
ajst-26903	103	27	on	on	ADP
ajst-26903	103	28	attention	attention	NOUN
ajst-26903	103	29	mechanism	mechanism	NOUN
ajst-26903	103	30	for	for	ADP
ajst-26903	103	31	well	well	ADJ
ajst-26903	103	32	production	production	NOUN
ajst-26903	103	33	prediction	prediction	NOUN
ajst-26903	103	34	.	.	PUNCT
ajst-26903	104	1	j.	j.	PROPN
ajst-26903	104	2	pet	pet	PROPN
ajst-26903	104	3	.	.	PUNCT
ajst-26903	105	1	sci	sci	PROPN
ajst-26903	105	2	.	.	PUNCT
ajst-26903	106	1	eng	eng	PROPN
ajst-26903	106	2	.	.	PROPN
ajst-26903	106	3	2022	2022	NUM
ajst-26903	106	4	,	,	PUNCT
ajst-26903	106	5	218	218	NUM
ajst-26903	106	6	,	,	PUNCT
ajst-26903	106	7	111043	111043	NUM
ajst-26903	106	8	[	[	X
ajst-26903	106	9	5	5	NUM
ajst-26903	106	10	]	]	X
ajst-26903	106	11	lara	lara	NOUN
ajst-26903	106	12	-	-	PUNCT
ajst-26903	106	13	benítez	benítez	NOUN
ajst-26903	106	14	,	,	PUNCT
ajst-26903	106	15	p.	p.	PROPN
ajst-26903	106	16	;	;	PUNCT
ajst-26903	106	17	carranza	carranza	PROPN
ajst-26903	106	18	-	-	PUNCT
ajst-26903	106	19	garcía	garcía	ADJ
ajst-26903	106	20	,	,	PUNCT
ajst-26903	106	21	m.	m.	NOUN
ajst-26903	106	22	;	;	PUNCT
ajst-26903	106	23	luna	luna	PROPN
ajst-26903	106	24	-	-	PUNCT
ajst-26903	106	25	romera	romera	NOUN
ajst-26903	106	26	,	,	PUNCT
ajst-26903	106	27	j.m	j.m	PROPN
ajst-26903	106	28	.	.	PROPN
ajst-26903	106	29	;	;	PUNCT
ajst-26903	106	30	riquelme	riquelme	PROPN
ajst-26903	106	31	,	,	PUNCT
ajst-26903	106	32	j.c	j.c	PROPN
ajst-26903	106	33	.	.	PROPN
ajst-26903	106	34	temporal	temporal	ADJ
ajst-26903	106	35	convolutional	convolutional	ADJ
ajst-26903	106	36	networks	network	NOUN
ajst-26903	106	37	applied	apply	VERB
ajst-26903	106	38	to	to	ADP
ajst-26903	106	39	energy	energy	NOUN
ajst-26903	106	40	-	-	PUNCT
ajst-26903	106	41	related	relate	VERB
ajst-26903	106	42	time	time	NOUN
ajst-26903	106	43	series	series	PROPN
ajst-26903	106	44	forecasting	forecasting	PROPN
ajst-26903	106	45	.	.	PUNCT
ajst-26903	107	1	appl	appl	PROPN
ajst-26903	107	2	.	.	PUNCT
ajst-26903	108	1	sci	sci	PROPN
ajst-26903	108	2	.	.	PROPN
ajst-26903	108	3	2020	2020	NUM
ajst-26903	108	4	,	,	PUNCT
ajst-26903	108	5	10	10	NUM
ajst-26903	108	6	,	,	PUNCT
ajst-26903	108	7	2322	2322	NUM
ajst-26903	108	8	.	.	PUNCT
ajst-26903	109	1	[	[	X
ajst-26903	109	2	6	6	NUM
ajst-26903	109	3	]	]	X
ajst-26903	109	4	wan	wan	PROPN
ajst-26903	109	5	,	,	PUNCT
ajst-26903	109	6	r.	r.	PROPN
ajst-26903	109	7	;	;	PUNCT
ajst-26903	109	8	tian	tian	PROPN
ajst-26903	109	9	,	,	PUNCT
ajst-26903	109	10	c.	c.	PROPN
ajst-26903	109	11	;	;	PUNCT
ajst-26903	109	12	zhang	zhang	PROPN
ajst-26903	109	13	,	,	PUNCT
ajst-26903	109	14	w.	w.	PROPN
ajst-26903	109	15	;	;	PUNCT
ajst-26903	109	16	deng	deng	PROPN
ajst-26903	109	17	,	,	PUNCT
ajst-26903	109	18	w.	w.	PROPN
ajst-26903	109	19	;	;	PUNCT
ajst-26903	109	20	yang	yang	PROPN
ajst-26903	109	21	,	,	PUNCT
ajst-26903	109	22	f.	f.	PROPN
ajst-26903	110	1	a	a	DET
ajst-26903	110	2	multivariate	multivariate	NOUN
ajst-26903	110	3	temporal	temporal	ADJ
ajst-26903	110	4	convolutional	convolutional	ADJ
ajst-26903	110	5	attention	attention	NOUN
ajst-26903	110	6	network	network	NOUN
ajst-26903	110	7	for	for	ADP
ajst-26903	110	8	time	time	NOUN
ajst-26903	110	9	-	-	PUNCT
ajst-26903	110	10	series	series	NOUN
ajst-26903	110	11	forecasting	forecasting	NOUN
ajst-26903	110	12	.	.	PUNCT
ajst-26903	111	1	electronics	electronic	NOUN
ajst-26903	111	2	2022	2022	NUM
ajst-26903	111	3	,	,	PUNCT
ajst-26903	111	4	11	11	NUM
ajst-26903	111	5	,	,	PUNCT
ajst-26903	111	6	1516	1516	NUM
ajst-26903	111	7	.	.	PUNCT
ajst-26903	112	1	[	[	X
ajst-26903	112	2	7	7	NUM
ajst-26903	112	3	]	]	SYM
ajst-26903	112	4	li	li	PROPN
ajst-26903	112	5	,	,	PUNCT
ajst-26903	112	6	d.	d.	PROPN
ajst-26903	112	7	;	;	PUNCT
ajst-26903	112	8	wang	wang	PROPN
ajst-26903	112	9	,	,	PUNCT
ajst-26903	112	10	z.	z.	PROPN
ajst-26903	112	11	;	;	PUNCT
ajst-26903	112	12	zha	zha	PROPN
ajst-26903	112	13	,	,	PUNCT
ajst-26903	112	14	w.	w.	PROPN
ajst-26903	112	15	;	;	PUNCT
ajst-26903	112	16	wang	wang	PROPN
ajst-26903	112	17	,	,	PUNCT
ajst-26903	112	18	j.	j.	PROPN
ajst-26903	112	19	;	;	PUNCT
ajst-26903	112	20	he	he	PRON
ajst-26903	112	21	,	,	PUNCT
ajst-26903	112	22	y.	y.	PROPN
ajst-26903	112	23	;	;	PUNCT
ajst-26903	112	24	huang	huang	PROPN
ajst-26903	112	25	,	,	PUNCT
ajst-26903	112	26	x.	x.	PROPN
ajst-26903	112	27	;	;	PUNCT
ajst-26903	112	28	du	du	X
ajst-26903	112	29	,	,	PUNCT
ajst-26903	112	30	y.	y.	NOUN
ajst-26903	112	31	predicting	predict	VERB
ajst-26903	112	32	production	production	NOUN
ajst-26903	112	33	-	-	PUNCT
ajst-26903	112	34	rate	rate	NOUN
ajst-26903	112	35	using	use	VERB
ajst-26903	112	36	wellhead	wellhead	NOUN
ajst-26903	112	37	pressure	pressure	NOUN
ajst-26903	112	38	for	for	ADP
ajst-26903	112	39	shale	shale	NOUN
ajst-26903	112	40	gas	gas	NOUN
ajst-26903	112	41	well	well	ADV
ajst-26903	112	42	based	base	VERB
ajst-26903	112	43	on	on	ADP
ajst-26903	112	44	temporal	temporal	ADJ
ajst-26903	112	45	convolutional	convolutional	ADJ
ajst-26903	112	46	network	network	NOUN
ajst-26903	112	47	.	.	PUNCT
ajst-26903	113	1	j.	j.	PROPN
ajst-26903	113	2	pet	pet	PROPN
ajst-26903	113	3	.	.	PUNCT
ajst-26903	114	1	sci	sci	PROPN
ajst-26903	114	2	.	.	PUNCT
ajst-26903	115	1	eng	eng	PROPN
ajst-26903	115	2	.	.	PROPN
ajst-26903	115	3	2022	2022	NUM
ajst-26903	115	4	,	,	PUNCT
ajst-26903	115	5	216	216	NUM
ajst-26903	115	6	,	,	PUNCT
ajst-26903	115	7	110644	110644	NUM
ajst-26903	115	8	.	.	PUNCT
ajst-26903	116	1	[	[	X
ajst-26903	116	2	8	8	NUM
ajst-26903	116	3	]	]	X
ajst-26903	116	4	ma	ma	PROPN
ajst-26903	116	5	,	,	PUNCT
ajst-26903	116	6	x.	x.	PROPN
ajst-26903	116	7	;	;	PUNCT
ajst-26903	116	8	hou	hou	PROPN
ajst-26903	116	9	,	,	PUNCT
ajst-26903	116	10	m.	m.	NOUN
ajst-26903	116	11	;	;	PUNCT
ajst-26903	116	12	zhan	zhan	PROPN
ajst-26903	116	13	,	,	PUNCT
ajst-26903	116	14	j.	j.	PROPN
ajst-26903	116	15	;	;	PUNCT
ajst-26903	116	16	zhong	zhong	PROPN
ajst-26903	116	17	,	,	PUNCT
ajst-26903	116	18	r.	r.	PROPN
ajst-26903	116	19	enhancing	enhance	VERB
ajst-26903	116	20	production	production	NOUN
ajst-26903	116	21	prediction	prediction	NOUN
ajst-26903	116	22	in	in	ADP
ajst-26903	116	23	shale	shale	NOUN
ajst-26903	116	24	gas	gas	NOUN
ajst-26903	116	25	reservoirs	reservoir	NOUN
ajst-26903	116	26	using	use	VERB
ajst-26903	116	27	a	a	DET
ajst-26903	116	28	hybrid	hybrid	ADJ
ajst-26903	116	29	gated	gate	VERB
ajst-26903	116	30	recurrent	recurrent	ADJ
ajst-26903	116	31	unit	unit	NOUN
ajst-26903	116	32	and	and	CCONJ
ajst-26903	116	33	multilayer	multilayer	ADJ
ajst-26903	116	34	perceptron	perceptron	PROPN
ajst-26903	116	35	(	(	PUNCT
ajst-26903	116	36	gru	gru	PROPN
ajst-26903	116	37	-	-	NOUN
ajst-26903	116	38	mlp	mlp	NOUN
ajst-26903	116	39	)	)	PUNCT
ajst-26903	116	40	model	model	NOUN
ajst-26903	116	41	.	.	PUNCT
ajst-26903	117	1	appl	appl	PROPN
ajst-26903	117	2	.	.	PUNCT
ajst-26903	118	1	sci	sci	PROPN
ajst-26903	118	2	.	.	PROPN
ajst-26903	118	3	2023	2023	NUM
ajst-26903	118	4	,	,	PUNCT
ajst-26903	118	5	13	13	NUM
ajst-26903	118	6	,	,	PUNCT
ajst-26903	118	7	9827	9827	NUM
ajst-26903	118	8	.	.	PUNCT