id	sid	tid	token	lemma	pos
cana-1051	1	1	communications	communication	NOUN
cana-1051	1	2	on	on	ADP
cana-1051	1	3	applied	apply	VERB
cana-1051	1	4	nonlinear	nonlinear	ADJ
cana-1051	1	5	analysis	analysis	NOUN
cana-1051	1	6	issn	issn	NOUN
cana-1051	1	7	:	:	PUNCT
cana-1051	1	8	1074	1074	NUM
cana-1051	1	9	-	-	PUNCT
cana-1051	1	10	133x	133x	NUM
cana-1051	1	11	vol	vol	NOUN
cana-1051	1	12	31	31	NUM
cana-1051	1	13	no	no	NOUN
cana-1051	1	14	.	.	PUNCT
cana-1051	2	1	5s	5s	NUM
cana-1051	2	2	(	(	PUNCT
cana-1051	2	3	2024	2024	NUM
cana-1051	2	4	)	)	PUNCT
cana-1051	2	5	301	301	NUM
cana-1051	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	2	7	prediction	prediction	NOUN
cana-1051	2	8	of	of	ADP
cana-1051	2	9	exchange	exchange	NOUN
cana-1051	2	10	rates	rate	NOUN
cana-1051	2	11	using	use	VERB
cana-1051	2	12	neural	neural	ADJ
cana-1051	2	13	networks	network	NOUN
cana-1051	2	14	and	and	CCONJ
cana-1051	2	15	performance	performance	NOUN
cana-1051	2	16	by	by	ADP
cana-1051	2	17	friedman	friedman	PROPN
cana-1051	2	18	’s	’s	PART
cana-1051	2	19	test	test	PROPN
cana-1051	2	20	ashwini1	ashwini1	PROPN
cana-1051	2	21	,	,	PUNCT
cana-1051	2	22	dr	dr	PROPN
cana-1051	2	23	.	.	PROPN
cana-1051	2	24	n.	n.	PROPN
cana-1051	2	25	konda	konda	PROPN
cana-1051	2	26	reddy2	reddy2	PROPN
cana-1051	2	27	,	,	PUNCT
cana-1051	2	28	dr	dr	PROPN
cana-1051	2	29	.	.	PROPN
cana-1051	2	30	k	k	PROPN
cana-1051	2	31	murali	murali	PROPN
cana-1051	2	32	krishna3	krishna3	PROPN
cana-1051	2	33	1	1	NUM
cana-1051	2	34	research	research	NOUN
cana-1051	2	35	scholar	scholar	NOUN
cana-1051	2	36	,	,	PUNCT
cana-1051	2	37	department	department	NOUN
cana-1051	2	38	of	of	ADP
cana-1051	2	39	engineering	engineering	NOUN
cana-1051	2	40	mathematics	mathematic	NOUN
cana-1051	2	41	,	,	PUNCT
cana-1051	2	42	k	k	PROPN
cana-1051	2	43	l	l	NOUN
cana-1051	2	44	e	e	PROPN
cana-1051	2	45	f	f	PROPN
cana-1051	2	46	,	,	PUNCT
cana-1051	2	47	vaddeswaram	vaddeswaram	PROPN
cana-1051	2	48	,	,	PUNCT
cana-1051	2	49	guntur	guntur	PROPN
cana-1051	2	50	dt	dt	PROPN
cana-1051	2	51	,	,	PUNCT
cana-1051	2	52	andhra	andhra	PROPN
cana-1051	2	53	pradesh	pradesh	PROPN
cana-1051	2	54	.	.	PUNCT
cana-1051	3	1	email	email	NOUN
cana-1051	3	2	:	:	PUNCT
cana-1051	3	3	ashwini.sunder@gmail.com	ashwini.sunder@gmail.com	X
cana-1051	3	4	2	2	NUM
cana-1051	3	5	associate	associate	NOUN
cana-1051	3	6	professor	professor	NOUN
cana-1051	3	7	,	,	PUNCT
cana-1051	3	8	department	department	NOUN
cana-1051	3	9	of	of	ADP
cana-1051	3	10	engineering	engineering	NOUN
cana-1051	3	11	mathematics	mathematic	NOUN
cana-1051	3	12	,	,	PUNCT
cana-1051	3	13	k	k	PROPN
cana-1051	3	14	l	l	NOUN
cana-1051	3	15	e	e	PROPN
cana-1051	3	16	f	f	PROPN
cana-1051	3	17	,	,	PUNCT
cana-1051	3	18	vaddeswaram	vaddeswaram	PROPN
cana-1051	3	19	,	,	PUNCT
cana-1051	3	20	guntur	guntur	PROPN
cana-1051	3	21	dt	dt	PROPN
cana-1051	3	22	,	,	PUNCT
cana-1051	3	23	andhra	andhra	PROPN
cana-1051	3	24	pradesh	pradesh	PROPN
cana-1051	3	25	.	.	PUNCT
cana-1051	4	1	email	email	NOUN
cana-1051	4	2	:	:	PUNCT
cana-1051	5	1	kondareddymamatha@gmail.com	kondareddymamatha@gmail.com	X
cana-1051	5	2	3assistant	3assistant	NUM
cana-1051	5	3	professor	professor	NOUN
cana-1051	5	4	,	,	PUNCT
cana-1051	5	5	department	department	NOUN
cana-1051	5	6	of	of	ADP
cana-1051	5	7	statistics	statistic	NOUN
cana-1051	5	8	,	,	PUNCT
cana-1051	5	9	g	g	PROPN
cana-1051	5	10	pulla	pulla	NOUN
cana-1051	5	11	reddy	reddy	PROPN
cana-1051	5	12	degree	degree	NOUN
cana-1051	5	13	and	and	CCONJ
cana-1051	5	14	pg	pg	ADP
cana-1051	5	15	college	college	NOUN
cana-1051	5	16	,	,	PUNCT
cana-1051	5	17	mehdipatnam	mehdipatnam	NOUN
cana-1051	5	18	,	,	PUNCT
cana-1051	5	19	hyderabad	hyderabad	PROPN
cana-1051	5	20	telangana	telangana	PROPN
cana-1051	5	21	.	.	PUNCT
cana-1051	6	1	email	email	NOUN
cana-1051	6	2	:	:	PUNCT
cana-1051	7	1	kmuralik68@gmail.com	kmuralik68@gmail.com	X
cana-1051	7	2	article	article	NOUN
cana-1051	7	3	history	history	NOUN
cana-1051	7	4	:	:	PUNCT
cana-1051	7	5	received	receive	VERB
cana-1051	7	6	:	:	PUNCT
cana-1051	7	7	15	15	NUM
cana-1051	7	8	-	-	SYM
cana-1051	7	9	05	05	NUM
cana-1051	7	10	-	-	PUNCT
cana-1051	7	11	2024	2024	NUM
cana-1051	7	12	revised	revise	VERB
cana-1051	7	13	:	:	PUNCT
cana-1051	7	14	25	25	NUM
cana-1051	7	15	-	-	PUNCT
cana-1051	7	16	06	06	NUM
cana-1051	7	17	-	-	PUNCT
cana-1051	7	18	2024	2024	NUM
cana-1051	7	19	accepted	accept	VERB
cana-1051	7	20	:	:	PUNCT
cana-1051	7	21	10	10	NUM
cana-1051	7	22	-	-	SYM
cana-1051	7	23	07	07	NUM
cana-1051	7	24	-	-	PUNCT
cana-1051	7	25	2024	2024	NUM
cana-1051	7	26	abstract	abstract	NOUN
cana-1051	7	27	:	:	PUNCT
cana-1051	7	28	forecasting	forecasting	NOUN
cana-1051	7	29	of	of	ADP
cana-1051	7	30	exchange	exchange	NOUN
cana-1051	7	31	rates	rate	NOUN
cana-1051	7	32	plays	play	VERB
cana-1051	7	33	a	a	DET
cana-1051	7	34	pivotal	pivotal	ADJ
cana-1051	7	35	role	role	NOUN
cana-1051	7	36	in	in	ADP
cana-1051	7	37	global	global	ADJ
cana-1051	7	38	trade	trade	NOUN
cana-1051	7	39	,	,	PUNCT
cana-1051	7	40	stocks	stock	NOUN
cana-1051	7	41	and	and	CCONJ
cana-1051	7	42	making	make	VERB
cana-1051	7	43	the	the	DET
cana-1051	7	44	policies	policy	NOUN
cana-1051	7	45	of	of	ADP
cana-1051	7	46	exports	export	NOUN
cana-1051	7	47	and	and	CCONJ
cana-1051	7	48	imports	import	NOUN
cana-1051	7	49	.	.	PUNCT
cana-1051	8	1	usd	usd	NOUN
cana-1051	8	2	exchange	exchange	NOUN
cana-1051	8	3	rates	rate	NOUN
cana-1051	8	4	used	use	VERB
cana-1051	8	5	widely	widely	ADV
cana-1051	8	6	for	for	ADP
cana-1051	8	7	many	many	ADJ
cana-1051	8	8	business	business	NOUN
cana-1051	8	9	areas	area	NOUN
cana-1051	8	10	.	.	PUNCT
cana-1051	9	1	in	in	ADP
cana-1051	9	2	this	this	DET
cana-1051	9	3	paper	paper	NOUN
cana-1051	9	4	an	an	DET
cana-1051	9	5	attempt	attempt	NOUN
cana-1051	9	6	is	be	AUX
cana-1051	9	7	made	make	VERB
cana-1051	9	8	to	to	PART
cana-1051	9	9	predict	predict	VERB
cana-1051	9	10	inr	inr	PROPN
cana-1051	9	11	/	/	SYM
cana-1051	9	12	usd	usd	NOUN
cana-1051	9	13	exchange	exchange	NOUN
cana-1051	9	14	rates	rate	NOUN
cana-1051	9	15	using	use	VERB
cana-1051	9	16	feed	feed	NOUN
cana-1051	9	17	forward	forward	ADV
cana-1051	9	18	neural	neural	ADJ
cana-1051	9	19	networks	network	NOUN
cana-1051	9	20	and	and	CCONJ
cana-1051	9	21	box	box	NOUN
cana-1051	9	22	-	-	PUNCT
cana-1051	9	23	jenkins	jenkins	PROPN
cana-1051	9	24	methodology	methodology	NOUN
cana-1051	9	25	.	.	PUNCT
cana-1051	10	1	the	the	DET
cana-1051	10	2	forecasting	forecasting	NOUN
cana-1051	10	3	performance	performance	NOUN
cana-1051	10	4	of	of	ADP
cana-1051	10	5	the	the	DET
cana-1051	10	6	developed	develop	VERB
cana-1051	10	7	models	model	NOUN
cana-1051	10	8	were	be	AUX
cana-1051	10	9	tested	test	VERB
cana-1051	10	10	using	use	VERB
cana-1051	10	11	error	error	NOUN
cana-1051	10	12	measures	measure	NOUN
cana-1051	10	13	like	like	ADP
cana-1051	10	14	mae	mae	PROPN
cana-1051	10	15	,	,	PUNCT
cana-1051	10	16	mape	mape	NOUN
cana-1051	10	17	and	and	CCONJ
cana-1051	10	18	rmse	rmse	NOUN
cana-1051	10	19	.	.	PUNCT
cana-1051	11	1	the	the	DET
cana-1051	11	2	results	result	NOUN
cana-1051	11	3	shows	show	VERB
cana-1051	11	4	ffnn	ffnn	NOUN
cana-1051	11	5	model	model	NOUN
cana-1051	11	6	has	have	VERB
cana-1051	11	7	better	well	ADJ
cana-1051	11	8	model	model	NOUN
cana-1051	11	9	than	than	ADP
cana-1051	11	10	arima	arima	PROPN
cana-1051	11	11	model	model	NOUN
cana-1051	11	12	.	.	PUNCT
cana-1051	12	1	the	the	DET
cana-1051	12	2	predicted	predict	VERB
cana-1051	12	3	exchange	exchange	NOUN
cana-1051	12	4	rates	rate	NOUN
cana-1051	12	5	would	would	AUX
cana-1051	12	6	vary	vary	VERB
cana-1051	12	7	between	between	ADP
cana-1051	12	8	83.06	83.06	NUM
cana-1051	12	9	and	and	CCONJ
cana-1051	12	10	83.28	83.28	NUM
cana-1051	12	11	for	for	ADP
cana-1051	12	12	the	the	DET
cana-1051	12	13	out	out	ADJ
cana-1051	12	14	sample	sample	NOUN
cana-1051	12	15	and	and	CCONJ
cana-1051	12	16	this	this	DET
cana-1051	12	17	variation	variation	NOUN
cana-1051	12	18	is	be	AUX
cana-1051	12	19	exchange	exchange	NOUN
cana-1051	12	20	rates	rate	NOUN
cana-1051	12	21	would	would	AUX
cana-1051	12	22	help	help	VERB
cana-1051	12	23	the	the	DET
cana-1051	12	24	business	business	NOUN
cana-1051	12	25	people	people	NOUN
cana-1051	12	26	and	and	CCONJ
cana-1051	12	27	also	also	ADV
cana-1051	12	28	for	for	ADP
cana-1051	12	29	framing	frame	VERB
cana-1051	12	30	the	the	DET
cana-1051	12	31	government	government	NOUN
cana-1051	12	32	policies	policy	NOUN
cana-1051	12	33	in	in	ADP
cana-1051	12	34	the	the	DET
cana-1051	12	35	future	future	NOUN
cana-1051	12	36	.	.	PUNCT
cana-1051	13	1	keywords	keyword	NOUN
cana-1051	13	2	:	:	PUNCT
cana-1051	13	3	exchange	exchange	NOUN
cana-1051	13	4	rates	rate	NOUN
cana-1051	13	5	,	,	PUNCT
cana-1051	13	6	box	box	PROPN
cana-1051	13	7	jenkins	jenkins	PROPN
cana-1051	13	8	methodology	methodology	PROPN
cana-1051	13	9	,	,	PUNCT
cana-1051	13	10	ffnn	ffnn	NOUN
cana-1051	13	11	,	,	PUNCT
cana-1051	13	12	mae	mae	PROPN
cana-1051	13	13	,	,	PUNCT
cana-1051	13	14	mape	mape	NOUN
cana-1051	13	15	and	and	CCONJ
cana-1051	13	16	rmse	rmse	ADJ
cana-1051	13	17	1	1	NUM
cana-1051	13	18	.	.	PUNCT
cana-1051	13	19	introduction	introduction	NOUN
cana-1051	13	20	the	the	DET
cana-1051	13	21	value	value	NOUN
cana-1051	13	22	of	of	ADP
cana-1051	13	23	one	one	NUM
cana-1051	13	24	currency	currency	NOUN
cana-1051	13	25	for	for	ADP
cana-1051	13	26	the	the	DET
cana-1051	13	27	purpose	purpose	NOUN
cana-1051	13	28	of	of	ADP
cana-1051	13	29	conversion	conversion	NOUN
cana-1051	13	30	to	to	ADP
cana-1051	13	31	another	another	PRON
cana-1051	13	32	is	be	AUX
cana-1051	13	33	called	call	VERB
cana-1051	13	34	exchange	exchange	NOUN
cana-1051	13	35	rate	rate	NOUN
cana-1051	13	36	.	.	PUNCT
cana-1051	14	1	in	in	ADP
cana-1051	14	2	finance	finance	NOUN
cana-1051	14	3	,	,	PUNCT
cana-1051	14	4	and	and	CCONJ
cana-1051	14	5	exchange	exchange	NOUN
cana-1051	14	6	rate	rate	NOUN
cana-1051	14	7	is	be	AUX
cana-1051	14	8	the	the	DET
cana-1051	14	9	rate	rate	NOUN
cana-1051	14	10	at	at	ADP
cana-1051	14	11	which	which	PRON
cana-1051	14	12	one	one	NUM
cana-1051	14	13	currency	currency	NOUN
cana-1051	14	14	will	will	AUX
cana-1051	14	15	be	be	AUX
cana-1051	14	16	exchanged	exchange	VERB
cana-1051	14	17	for	for	ADP
cana-1051	14	18	another	another	PRON
cana-1051	14	19	.	.	PUNCT
cana-1051	15	1	reserve	reserve	PROPN
cana-1051	15	2	bank	bank	PROPN
cana-1051	15	3	of	of	ADP
cana-1051	15	4	india	india	PROPN
cana-1051	15	5	buys	buy	VERB
cana-1051	15	6	foreign	foreign	ADJ
cana-1051	15	7	exchange	exchange	NOUN
cana-1051	15	8	when	when	SCONJ
cana-1051	15	9	the	the	DET
cana-1051	15	10	exchange	exchange	NOUN
cana-1051	15	11	rate	rate	NOUN
cana-1051	15	12	is	be	AUX
cana-1051	15	13	low	low	ADJ
cana-1051	15	14	and	and	CCONJ
cana-1051	15	15	sells	sell	VERB
cana-1051	15	16	the	the	DET
cana-1051	15	17	same	same	ADJ
cana-1051	15	18	when	when	SCONJ
cana-1051	15	19	it	it	PRON
cana-1051	15	20	is	be	AUX
cana-1051	15	21	sufficiently	sufficiently	ADV
cana-1051	15	22	high	high	ADJ
cana-1051	15	23	.	.	PUNCT
cana-1051	16	1	the	the	DET
cana-1051	16	2	exchange	exchange	NOUN
cana-1051	16	3	rate	rate	NOUN
cana-1051	16	4	is	be	AUX
cana-1051	16	5	a	a	DET
cana-1051	16	6	key	key	ADJ
cana-1051	16	7	financial	financial	ADJ
cana-1051	16	8	variable	variable	NOUN
cana-1051	16	9	affects	affect	VERB
cana-1051	16	10	decisions	decision	NOUN
cana-1051	16	11	made	make	VERB
cana-1051	16	12	by	by	ADP
cana-1051	16	13	foreign	foreign	ADJ
cana-1051	16	14	exchange	exchange	NOUN
cana-1051	16	15	investor	investor	NOUN
cana-1051	16	16	,	,	PUNCT
cana-1051	16	17	exporters	exporter	NOUN
cana-1051	16	18	,	,	PUNCT
cana-1051	16	19	importers	importer	NOUN
cana-1051	16	20	,	,	PUNCT
cana-1051	16	21	banks	bank	NOUN
cana-1051	16	22	,	,	PUNCT
cana-1051	16	23	business	business	NOUN
cana-1051	16	24	,	,	PUNCT
cana-1051	16	25	financial	financial	ADJ
cana-1051	16	26	institutions	institution	NOUN
cana-1051	16	27	,	,	PUNCT
cana-1051	16	28	policy	policy	NOUN
cana-1051	16	29	makers	maker	NOUN
cana-1051	16	30	and	and	CCONJ
cana-1051	16	31	tourists	tourist	NOUN
cana-1051	16	32	in	in	ADP
cana-1051	16	33	the	the	DET
cana-1051	16	34	developed	develop	VERB
cana-1051	16	35	as	as	ADV
cana-1051	16	36	well	well	ADV
cana-1051	16	37	as	as	ADP
cana-1051	16	38	developing	develop	VERB
cana-1051	16	39	world	world	NOUN
cana-1051	16	40	.	.	PUNCT
cana-1051	17	1	foreign	foreign	ADJ
cana-1051	17	2	exchange	exchange	NOUN
cana-1051	17	3	rates	rate	NOUN
cana-1051	17	4	are	be	AUX
cana-1051	17	5	affected	affect	VERB
cana-1051	17	6	by	by	ADP
cana-1051	17	7	many	many	ADJ
cana-1051	17	8	highly	highly	ADV
cana-1051	17	9	correlated	correlate	VERB
cana-1051	17	10	economic	economic	ADJ
cana-1051	17	11	,	,	PUNCT
cana-1051	17	12	political	political	ADJ
cana-1051	17	13	and	and	CCONJ
cana-1051	17	14	even	even	ADV
cana-1051	17	15	psychological	psychological	ADJ
cana-1051	17	16	factors	factor	NOUN
cana-1051	17	17	.	.	PUNCT
cana-1051	18	1	the	the	DET
cana-1051	18	2	interaction	interaction	NOUN
cana-1051	18	3	of	of	ADP
cana-1051	18	4	these	these	DET
cana-1051	18	5	factors	factor	NOUN
cana-1051	18	6	is	be	AUX
cana-1051	18	7	in	in	ADP
cana-1051	18	8	a	a	DET
cana-1051	18	9	very	very	ADV
cana-1051	18	10	complex	complex	ADJ
cana-1051	18	11	fashion	fashion	NOUN
cana-1051	18	12	.	.	PUNCT
cana-1051	19	1	therefore	therefore	ADV
cana-1051	19	2	to	to	PART
cana-1051	19	3	forecast	forecast	VERB
cana-1051	19	4	the	the	DET
cana-1051	19	5	changes	change	NOUN
cana-1051	19	6	of	of	ADP
cana-1051	19	7	foreign	foreign	ADJ
cana-1051	19	8	exchange	exchange	NOUN
cana-1051	19	9	rates	rate	NOUN
cana-1051	19	10	is	be	AUX
cana-1051	19	11	generally	generally	ADV
cana-1051	19	12	very	very	ADV
cana-1051	19	13	difficult	difficult	ADJ
cana-1051	19	14	.	.	PUNCT
cana-1051	20	1	researchers	researcher	NOUN
cana-1051	20	2	and	and	CCONJ
cana-1051	20	3	practioners	practioner	NOUN
cana-1051	20	4	have	have	AUX
cana-1051	20	5	been	be	AUX
cana-1051	20	6	striving	strive	VERB
cana-1051	20	7	for	for	ADP
cana-1051	20	8	an	an	DET
cana-1051	20	9	explanation	explanation	NOUN
cana-1051	20	10	of	of	ADP
cana-1051	20	11	movement	movement	NOUN
cana-1051	20	12	of	of	ADP
cana-1051	20	13	exchange	exchange	NOUN
cana-1051	20	14	rates	rate	NOUN
cana-1051	20	15	.	.	PUNCT
cana-1051	21	1	thus	thus	ADV
cana-1051	21	2	various	various	ADJ
cana-1051	21	3	kinds	kind	NOUN
cana-1051	21	4	of	of	ADP
cana-1051	21	5	forecasting	forecasting	NOUN
cana-1051	21	6	methods	method	NOUN
cana-1051	21	7	have	have	AUX
cana-1051	21	8	been	be	AUX
cana-1051	21	9	developed	develop	VERB
cana-1051	21	10	by	by	ADP
cana-1051	21	11	many	many	ADJ
cana-1051	21	12	researchers	researcher	NOUN
cana-1051	21	13	and	and	CCONJ
cana-1051	21	14	experts	expert	NOUN
cana-1051	21	15	.	.	PUNCT
cana-1051	22	1	currency	currency	NOUN
cana-1051	22	2	forecasts	forecast	NOUN
cana-1051	22	3	are	be	AUX
cana-1051	22	4	useful	useful	ADJ
cana-1051	22	5	in	in	ADP
cana-1051	22	6	the	the	DET
cana-1051	22	7	international	international	ADJ
cana-1051	22	8	aspects	aspect	NOUN
cana-1051	22	9	of	of	ADP
cana-1051	22	10	project	project	NOUN
cana-1051	22	11	evaluation	evaluation	NOUN
cana-1051	22	12	,	,	PUNCT
cana-1051	22	13	strategic	strategic	ADJ
cana-1051	22	14	planning	planning	NOUN
cana-1051	22	15	,	,	PUNCT
cana-1051	22	16	pricing	pricing	NOUN
cana-1051	22	17	,	,	PUNCT
cana-1051	22	18	working	work	VERB
cana-1051	22	19	capital	capital	NOUN
cana-1051	22	20	management	management	NOUN
cana-1051	22	21	and	and	CCONJ
cana-1051	22	22	the	the	DET
cana-1051	22	23	analysis	analysis	NOUN
cana-1051	22	24	of	of	ADP
cana-1051	22	25	portfolio	portfolio	NOUN
cana-1051	22	26	investments	investment	NOUN
cana-1051	22	27	.	.	PUNCT
cana-1051	23	1	most	most	ADJ
cana-1051	23	2	conventional	conventional	ADJ
cana-1051	23	3	econometric	econometric	ADJ
cana-1051	23	4	models	model	NOUN
cana-1051	23	5	are	be	AUX
cana-1051	23	6	not	not	PART
cana-1051	23	7	able	able	ADJ
cana-1051	23	8	to	to	PART
cana-1051	23	9	forecast	forecast	VERB
cana-1051	23	10	exchange	exchange	NOUN
cana-1051	23	11	rates	rate	NOUN
cana-1051	23	12	with	with	ADP
cana-1051	23	13	significantly	significantly	ADV
cana-1051	23	14	higher	high	ADJ
cana-1051	23	15	accuracy	accuracy	NOUN
cana-1051	23	16	.	.	PUNCT
cana-1051	24	1	in	in	ADP
cana-1051	24	2	recent	recent	ADJ
cana-1051	24	3	years	year	NOUN
cana-1051	24	4	,	,	PUNCT
cana-1051	24	5	there	there	PRON
cana-1051	24	6	has	have	AUX
cana-1051	24	7	been	be	AUX
cana-1051	24	8	a	a	DET
cana-1051	24	9	growing	grow	VERB
cana-1051	24	10	interest	interest	NOUN
cana-1051	24	11	to	to	PART
cana-1051	24	12	opt	opt	VERB
cana-1051	24	13	the	the	DET
cana-1051	24	14	state	state	NOUN
cana-1051	24	15	–	–	PUNCT
cana-1051	24	16	of	of	ADP
cana-1051	24	17	–	–	PUNCT
cana-1051	24	18	the	the	DET
cana-1051	24	19	–	–	PUNCT
cana-1051	24	20	art	art	NOUN
cana-1051	24	21	artificial	artificial	ADJ
cana-1051	24	22	intelligence	intelligence	NOUN
cana-1051	24	23	technology	technology	NOUN
cana-1051	24	24	to	to	PART
cana-1051	24	25	solve	solve	VERB
cana-1051	24	26	the	the	DET
cana-1051	24	27	problem	problem	NOUN
cana-1051	24	28	.	.	PUNCT
cana-1051	25	1	one	one	NUM
cana-1051	25	2	stream	stream	NOUN
cana-1051	25	3	of	of	ADP
cana-1051	25	4	these	these	DET
cana-1051	25	5	advanced	advance	VERB
cana-1051	25	6	techniques	technique	NOUN
cana-1051	25	7	communications	communication	NOUN
cana-1051	25	8	on	on	ADP
cana-1051	25	9	applied	apply	VERB
cana-1051	25	10	nonlinear	nonlinear	ADJ
cana-1051	25	11	analysis	analysis	NOUN
cana-1051	25	12	issn	issn	NOUN
cana-1051	25	13	:	:	PUNCT
cana-1051	25	14	1074	1074	NUM
cana-1051	25	15	-	-	PUNCT
cana-1051	25	16	133x	133x	NUM
cana-1051	25	17	vol	vol	NOUN
cana-1051	25	18	31	31	NUM
cana-1051	25	19	no	no	NOUN
cana-1051	25	20	.	.	PUNCT
cana-1051	26	1	5s	5s	NUM
cana-1051	26	2	(	(	PUNCT
cana-1051	26	3	2024	2024	NUM
cana-1051	26	4	)	)	PUNCT
cana-1051	26	5	302	302	NUM
cana-1051	26	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	26	7	focuses	focus	VERB
cana-1051	26	8	on	on	ADP
cana-1051	26	9	the	the	DET
cana-1051	26	10	use	use	NOUN
cana-1051	26	11	of	of	ADP
cana-1051	26	12	artificial	artificial	ADJ
cana-1051	26	13	neural	neural	ADJ
cana-1051	26	14	networks	network	NOUN
cana-1051	26	15	(	(	PUNCT
cana-1051	26	16	ann	ann	PROPN
cana-1051	26	17	)	)	PUNCT
cana-1051	26	18	to	to	PART
cana-1051	26	19	analyse	analyse	VERB
cana-1051	26	20	the	the	DET
cana-1051	26	21	future	future	ADJ
cana-1051	26	22	movements	movement	NOUN
cana-1051	26	23	in	in	ADP
cana-1051	26	24	the	the	DET
cana-1051	26	25	foreign	foreign	ADJ
cana-1051	26	26	exchange	exchange	NOUN
cana-1051	26	27	market	market	NOUN
cana-1051	26	28	.	.	PUNCT
cana-1051	27	1	neural	neural	ADJ
cana-1051	27	2	networks	network	NOUN
cana-1051	27	3	applications	application	NOUN
cana-1051	27	4	in	in	ADP
cana-1051	27	5	time	time	NOUN
cana-1051	27	6	series	series	PROPN
cana-1051	27	7	forecasting	forecasting	NOUN
cana-1051	27	8	is	be	AUX
cana-1051	27	9	discussed	discuss	VERB
cana-1051	27	10	by	by	ADP
cana-1051	27	11	krishna	krishna	PROPN
cana-1051	27	12	reddy	reddy	PROPN
cana-1051	27	13	and	and	CCONJ
cana-1051	27	14	kalyani	kalyani	PROPN
cana-1051	27	15	2005[4	2005[4	NUM
cana-1051	27	16	]	]	PUNCT
cana-1051	27	17	and	and	CCONJ
cana-1051	27	18	zhang	zhang	PROPN
cana-1051	27	19	et	et	PROPN
cana-1051	27	20	-	-	PUNCT
cana-1051	27	21	al1998	al1998	PROPN
cana-1051	28	1	[	[	X
cana-1051	28	2	13	13	NUM
cana-1051	28	3	]	]	PUNCT
cana-1051	28	4	.	.	PUNCT
cana-1051	29	1	modelling	model	VERB
cana-1051	29	2	exchange	exchange	NOUN
cana-1051	29	3	rates	rate	NOUN
cana-1051	29	4	using	use	VERB
cana-1051	29	5	boxjenkins	boxjenkin	NOUN
cana-1051	29	6	methodology	methodology	NOUN
cana-1051	29	7	,	,	PUNCT
cana-1051	29	8	neural	neural	ADJ
cana-1051	29	9	networks	network	NOUN
cana-1051	29	10	and	and	CCONJ
cana-1051	29	11	its	its	PRON
cana-1051	29	12	applications	application	NOUN
cana-1051	29	13	presented	present	VERB
cana-1051	29	14	by	by	ADP
cana-1051	29	15	huang	huang	PROPN
cana-1051	29	16	and	and	CCONJ
cana-1051	29	17	lai	lai	PROPN
cana-1051	30	1	2004[2	2004[2	NUM
cana-1051	30	2	]	]	X
cana-1051	31	1	kuan	kuan	PROPN
cana-1051	31	2	and	and	CCONJ
cana-1051	31	3	liu	liu	PROPN
cana-1051	31	4	,	,	PUNCT
cana-1051	31	5	1995[5	1995[5	NUM
cana-1051	31	6	]	]	PUNCT
cana-1051	31	7	,	,	PUNCT
cana-1051	31	8	kamruzzaman,2004[3	kamruzzaman,2004[3	NOUN
cana-1051	31	9	]	]	X
cana-1051	31	10	.	.	PUNCT
cana-1051	32	1	2	2	X
cana-1051	32	2	.	.	X
cana-1051	32	3	review	review	NOUN
cana-1051	32	4	of	of	ADP
cana-1051	32	5	forcasting	forcaste	VERB
cana-1051	32	6	methods	method	NOUN
cana-1051	32	7	2.1	2.1	NUM
cana-1051	32	8	box	box	NOUN
cana-1051	32	9	-	-	PUNCT
cana-1051	32	10	jenkins	jenkin	NOUN
cana-1051	32	11	methodology	methodology	NOUN
cana-1051	32	12	in	in	ADP
cana-1051	32	13	this	this	DET
cana-1051	32	14	section	section	NOUN
cana-1051	32	15	,	,	PUNCT
cana-1051	32	16	the	the	DET
cana-1051	32	17	modelling	modelling	NOUN
cana-1051	32	18	of	of	ADP
cana-1051	32	19	exchange	exchange	NOUN
cana-1051	32	20	rates	rate	NOUN
cana-1051	32	21	inr	inr	NOUN
cana-1051	32	22	-	-	PUNCT
cana-1051	32	23	usd	usd	NOUN
cana-1051	32	24	in	in	ADP
cana-1051	32	25	india	india	PROPN
cana-1051	32	26	per	per	ADP
cana-1051	32	27	10gm	10gm	NOUN
cana-1051	32	28	using	use	VERB
cana-1051	32	29	box	box	NOUN
cana-1051	32	30	-	-	PUNCT
cana-1051	32	31	jenkins	jenkin	NOUN
cana-1051	32	32	methodology	methodology	NOUN
cana-1051	32	33	is	be	AUX
cana-1051	32	34	discussed	discuss	VERB
cana-1051	32	35	.	.	PUNCT
cana-1051	33	1	the	the	DET
cana-1051	33	2	box	box	NOUN
cana-1051	33	3	-	-	PUNCT
cana-1051	33	4	jenkins	jenkin	NOUN
cana-1051	33	5	procedure	procedure	NOUN
cana-1051	33	6	is	be	AUX
cana-1051	33	7	concerned	concern	VERB
cana-1051	33	8	with	with	ADP
cana-1051	33	9	the	the	DET
cana-1051	33	10	fitting	fitting	NOUN
cana-1051	33	11	of	of	ADP
cana-1051	33	12	an	an	DET
cana-1051	33	13	arima	arima	NOUN
cana-1051	33	14	model	model	NOUN
cana-1051	33	15	of	of	ADP
cana-1051	33	16	the	the	DET
cana-1051	33	17	following	follow	VERB
cana-1051	33	18	form	form	NOUN
cana-1051	33	19	for	for	ADP
cana-1051	33	20	the	the	DET
cana-1051	33	21	a	a	DET
cana-1051	33	22	given	give	VERB
cana-1051	33	23	set	set	NOUN
cana-1051	33	24	of	of	ADP
cana-1051	33	25	data	datum	NOUN
cana-1051	33	26	{	{	PUNCT
cana-1051	33	27	zt	zt	NOUN
cana-1051	33	28	:	:	PUNCT
cana-1051	33	29	t=1	t=1	PROPN
cana-1051	33	30	,	,	PUNCT
cana-1051	33	31	2	2	NUM
cana-1051	33	32	,	,	PUNCT
cana-1051	33	33	…	…	PUNCT
cana-1051	33	34	…	…	PUNCT
cana-1051	33	35	..	..	PUNCT
cana-1051	33	36	n	n	CCONJ
cana-1051	33	37	}	}	PUNCT
cana-1051	33	38	and	and	CCONJ
cana-1051	33	39	the	the	DET
cana-1051	33	40	general	general	ADJ
cana-1051	33	41	form	form	NOUN
cana-1051	33	42	of	of	ADP
cana-1051	33	43	arima	arima	PROPN
cana-1051	33	44	(	(	PUNCT
cana-1051	33	45	p	p	X
cana-1051	33	46	,	,	PUNCT
cana-1051	33	47	d	d	NOUN
cana-1051	33	48	,	,	PUNCT
cana-1051	33	49	q	q	NOUN
cana-1051	33	50	)	)	PUNCT
cana-1051	33	51	model	model	NOUN
cana-1051	33	52	is	be	AUX
cana-1051	33	53	given	give	VERB
cana-1051	33	54	by	by	ADP
cana-1051	33	55	∅(𝐵)∇dzt	∅(𝐵)∇dzt	NOUN
cana-1051	33	56	=	=	PUNCT
cana-1051	33	57	θ(b)at	θ(b)at	PROPN
cana-1051	33	58	where	where	SCONJ
cana-1051	33	59	∅(𝐵	∅(𝐵	VERB
cana-1051	33	60	)	)	PUNCT
cana-1051	33	61	=	=	SYM
cana-1051	34	1	1	1	NUM
cana-1051	34	2	−	−	NOUN
cana-1051	34	3	∅1𝐵	∅1𝐵	ADJ
cana-1051	34	4	−	−	PROPN
cana-1051	34	5	∅2𝐵2	∅2𝐵2	NOUN
cana-1051	34	6	−	−	PROPN
cana-1051	34	7	−	−	NOUN
cana-1051	34	8	−	−	NOUN
cana-1051	34	9	−	−	NOUN
cana-1051	34	10	−	−	NOUN
cana-1051	34	11	∅𝑝𝐵𝑝	∅𝑝𝐵𝑝	NOUN
cana-1051	34	12	𝜃(𝐵	𝜃(𝐵	NUM
cana-1051	34	13	)	)	PUNCT
cana-1051	34	14	=	=	SYM
cana-1051	34	15	1	1	NUM
cana-1051	34	16	−	−	NOUN
cana-1051	34	17	𝜃1𝐵	𝜃1𝐵	SYM
cana-1051	34	18	−	−	PROPN
cana-1051	34	19	𝜃2𝐵2	𝜃2𝐵2	PUNCT
cana-1051	34	20	−	−	PROPN
cana-1051	34	21	−	−	PROPN
cana-1051	34	22	−	−	PROPN
cana-1051	34	23	−𝜃𝑞𝐵𝑞	−𝜃𝑞𝐵𝑞	NOUN
cana-1051	34	24	and	and	CCONJ
cana-1051	34	25	∇𝑑=	∇𝑑=	NOUN
cana-1051	34	26	(	(	PUNCT
cana-1051	34	27	1	1	NUM
cana-1051	34	28	−	−	NOUN
cana-1051	34	29	𝐵)𝑑	𝐵)𝑑	NOUN
cana-1051	34	30	where	where	SCONJ
cana-1051	34	31	𝐵𝐾𝑍𝑡	𝐵𝐾𝑍𝑡	NOUN
cana-1051	34	32	=	=	SYM
cana-1051	34	33	𝑍𝑡−𝑘	𝑍𝑡−𝑘	X
cana-1051	34	34	𝑎𝑛𝑑	𝑎𝑛𝑑	X
cana-1051	34	35	𝑎𝑡	𝑎𝑡	PROPN
cana-1051	34	36	is	be	AUX
cana-1051	34	37	a	a	DET
cana-1051	34	38	white	white	ADJ
cana-1051	34	39	noise	noise	NOUN
cana-1051	34	40	process	process	NOUN
cana-1051	34	41	with	with	ADP
cana-1051	34	42	zero	zero	NUM
cana-1051	34	43	mean	mean	NOUN
cana-1051	34	44	and	and	CCONJ
cana-1051	34	45	variance	variance	NOUN
cana-1051	34	46	𝜎𝑎	𝜎𝑎	NOUN
cana-1051	34	47	2	2	X
cana-1051	34	48	.	.	PUNCT
cana-1051	35	1	the	the	DET
cana-1051	35	2	boxjenkins	boxjenkin	NOUN
cana-1051	35	3	procedure	procedure	NOUN
cana-1051	35	4	consists	consist	VERB
cana-1051	35	5	of	of	ADP
cana-1051	35	6	the	the	DET
cana-1051	35	7	following	follow	VERB
cana-1051	35	8	four	four	NUM
cana-1051	35	9	stages	stage	NOUN
cana-1051	35	10	.	.	PUNCT
cana-1051	36	1	(	(	PUNCT
cana-1051	36	2	i	i	NOUN
cana-1051	36	3	)	)	PUNCT
cana-1051	36	4	model	model	NOUN
cana-1051	36	5	identification	identification	NOUN
cana-1051	36	6	,	,	PUNCT
cana-1051	36	7	where	where	SCONJ
cana-1051	36	8	the	the	DET
cana-1051	36	9	orders	order	NOUN
cana-1051	36	10	d	d	NOUN
cana-1051	36	11	,	,	PUNCT
cana-1051	36	12	p	p	X
cana-1051	36	13	,	,	PUNCT
cana-1051	36	14	q	q	X
cana-1051	36	15	are	be	AUX
cana-1051	36	16	determined	determine	VERB
cana-1051	36	17	by	by	ADP
cana-1051	36	18	observing	observe	VERB
cana-1051	36	19	the	the	DET
cana-1051	36	20	behavior	behavior	NOUN
cana-1051	36	21	of	of	ADP
cana-1051	36	22	the	the	DET
cana-1051	36	23	corresponding	corresponding	ADJ
cana-1051	36	24	autocorrelation	autocorrelation	NOUN
cana-1051	36	25	function	function	NOUN
cana-1051	36	26	(	(	PUNCT
cana-1051	36	27	acf	acf	PROPN
cana-1051	36	28	)	)	PUNCT
cana-1051	36	29	and	and	CCONJ
cana-1051	36	30	partial	partial	ADJ
cana-1051	36	31	autocorrelation	autocorrelation	NOUN
cana-1051	36	32	function	function	NOUN
cana-1051	36	33	(	(	PUNCT
cana-1051	36	34	pacf	pacf	NOUN
cana-1051	36	35	)	)	PUNCT
cana-1051	36	36	.	.	PUNCT
cana-1051	37	1	(	(	PUNCT
cana-1051	37	2	ii	ii	NOUN
cana-1051	37	3	)	)	PUNCT
cana-1051	37	4	estimation	estimation	NOUN
cana-1051	37	5	,	,	PUNCT
cana-1051	37	6	where	where	SCONJ
cana-1051	37	7	the	the	DET
cana-1051	37	8	parameters	parameter	NOUN
cana-1051	37	9	of	of	ADP
cana-1051	37	10	the	the	DET
cana-1051	37	11	model	model	NOUN
cana-1051	37	12	are	be	AUX
cana-1051	37	13	estimated	estimate	VERB
cana-1051	37	14	by	by	ADP
cana-1051	37	15	the	the	DET
cana-1051	37	16	maximum	maximum	ADJ
cana-1051	37	17	likelihood	likelihood	NOUN
cana-1051	37	18	method	method	NOUN
cana-1051	37	19	.	.	PUNCT
cana-1051	38	1	(	(	PUNCT
cana-1051	38	2	iii	iii	X
cana-1051	38	3	)	)	PUNCT
cana-1051	38	4	diagnostic	diagnostic	ADJ
cana-1051	38	5	checking	checking	NOUN
cana-1051	38	6	by	by	ADP
cana-1051	38	7	the	the	DET
cana-1051	38	8	“	"	PUNCT
cana-1051	38	9	portmanteau	portmanteau	NOUN
cana-1051	38	10	test	test	NOUN
cana-1051	38	11	”	"	PUNCT
cana-1051	38	12	,	,	PUNCT
cana-1051	38	13	where	where	SCONJ
cana-1051	38	14	the	the	DET
cana-1051	38	15	adequacy	adequacy	NOUN
cana-1051	38	16	of	of	ADP
cana-1051	38	17	the	the	DET
cana-1051	38	18	fitted	fit	VERB
cana-1051	38	19	model	model	NOUN
cana-1051	38	20	is	be	AUX
cana-1051	38	21	checked	check	VERB
cana-1051	38	22	by	by	ADP
cana-1051	38	23	the	the	DET
cana-1051	38	24	ljung	ljung	PROPN
cana-1051	38	25	-	-	PUNCT
cana-1051	38	26	box	box	NOUN
cana-1051	38	27	statistic	statistic	NOUN
cana-1051	38	28	,	,	PUNCT
cana-1051	38	29	applied	apply	VERB
cana-1051	38	30	to	to	ADP
cana-1051	38	31	the	the	DET
cana-1051	38	32	residual	residual	NOUN
cana-1051	38	33	of	of	ADP
cana-1051	38	34	the	the	DET
cana-1051	38	35	model	model	NOUN
cana-1051	38	36	.	.	PUNCT
cana-1051	39	1	(	(	PUNCT
cana-1051	39	2	iv	iv	X
cana-1051	39	3	)	)	PUNCT
cana-1051	39	4	forecast	forecast	NOUN
cana-1051	39	5	is	be	AUX
cana-1051	39	6	obtained	obtain	VERB
cana-1051	39	7	from	from	ADP
cana-1051	39	8	an	an	DET
cana-1051	39	9	adequate	adequate	ADJ
cana-1051	39	10	model	model	NOUN
cana-1051	39	11	using	use	VERB
cana-1051	39	12	minimum	minimum	NOUN
cana-1051	39	13	mean	mean	NOUN
cana-1051	39	14	squared	square	VERB
cana-1051	39	15	error	error	NOUN
cana-1051	39	16	method	method	NOUN
cana-1051	39	17	.	.	PUNCT
cana-1051	40	1	if	if	SCONJ
cana-1051	40	2	the	the	DET
cana-1051	40	3	model	model	NOUN
cana-1051	40	4	is	be	AUX
cana-1051	40	5	judged	judge	VERB
cana-1051	40	6	to	to	PART
cana-1051	40	7	be	be	AUX
cana-1051	40	8	inadequate	inadequate	ADJ
cana-1051	40	9	,	,	PUNCT
cana-1051	40	10	stages	stage	NOUN
cana-1051	40	11	(	(	PUNCT
cana-1051	40	12	i	i	NOUN
cana-1051	40	13	)	)	PUNCT
cana-1051	40	14	to	to	ADP
cana-1051	40	15	(	(	PUNCT
cana-1051	40	16	iii	iii	X
cana-1051	40	17	)	)	PUNCT
cana-1051	40	18	are	be	AUX
cana-1051	40	19	repeated	repeat	VERB
cana-1051	40	20	with	with	ADP
cana-1051	40	21	different	different	ADJ
cana-1051	40	22	values	value	NOUN
cana-1051	40	23	of	of	ADP
cana-1051	40	24	d	d	PROPN
cana-1051	40	25	,	,	PUNCT
cana-1051	40	26	p	p	X
cana-1051	40	27	,	,	PUNCT
cana-1051	40	28	and	and	CCONJ
cana-1051	40	29	q	q	X
cana-1051	40	30	until	until	SCONJ
cana-1051	40	31	an	an	DET
cana-1051	40	32	adequate	adequate	ADJ
cana-1051	40	33	model	model	NOUN
cana-1051	40	34	is	be	AUX
cana-1051	40	35	obtained	obtain	VERB
cana-1051	40	36	l	l	PROPN
cana-1051	40	37	jung	jung	PROPN
cana-1051	40	38	g.m	g.m	PROPN
cana-1051	40	39	.	.	PROPN
cana-1051	40	40	et	et	PROPN
cana-1051	40	41	al	al	PROPN
cana-1051	40	42	.	.	PROPN
cana-1051	40	43	,	,	PUNCT
cana-1051	40	44	1979	1979	NUM
cana-1051	41	1	[	[	X
cana-1051	41	2	9	9	NUM
cana-1051	41	3	]	]	PUNCT
cana-1051	41	4	.	.	PUNCT
cana-1051	42	1	2.2	2.2	NUM
cana-1051	42	2	neural	neural	ADJ
cana-1051	42	3	networks	network	NOUN
cana-1051	42	4	model	model	VERB
cana-1051	42	5	an	an	DET
cana-1051	42	6	artificial	artificial	ADJ
cana-1051	42	7	neural	neural	ADJ
cana-1051	42	8	networks	network	NOUN
cana-1051	42	9	(	(	PUNCT
cana-1051	42	10	ann	ann	PROPN
cana-1051	42	11	)	)	PUNCT
cana-1051	42	12	is	be	AUX
cana-1051	42	13	a	a	DET
cana-1051	42	14	mathematical	mathematical	ADJ
cana-1051	42	15	model	model	NOUN
cana-1051	42	16	which	which	PRON
cana-1051	42	17	is	be	AUX
cana-1051	42	18	inspired	inspire	VERB
cana-1051	42	19	by	by	ADP
cana-1051	42	20	the	the	DET
cana-1051	42	21	structure	structure	NOUN
cana-1051	42	22	and	and	CCONJ
cana-1051	42	23	functional	functional	ADJ
cana-1051	42	24	aspects	aspect	NOUN
cana-1051	42	25	of	of	ADP
cana-1051	42	26	biological	biological	ADJ
cana-1051	42	27	neural	neural	ADJ
cana-1051	42	28	networks	network	NOUN
cana-1051	42	29	is	be	AUX
cana-1051	42	30	a	a	DET
cana-1051	42	31	powerful	powerful	ADJ
cana-1051	42	32	forecasting	forecasting	NOUN
cana-1051	42	33	model	model	NOUN
cana-1051	42	34	.	.	PUNCT
cana-1051	43	1	it	it	PRON
cana-1051	43	2	consists	consist	VERB
cana-1051	43	3	of	of	ADP
cana-1051	43	4	an	an	DET
cana-1051	43	5	interconnected	interconnected	ADJ
cana-1051	43	6	group	group	NOUN
cana-1051	43	7	of	of	ADP
cana-1051	43	8	artificial	artificial	ADJ
cana-1051	43	9	neurons	neuron	NOUN
cana-1051	43	10	,	,	PUNCT
cana-1051	43	11	and	and	CCONJ
cana-1051	43	12	it	it	PRON
cana-1051	43	13	processes	process	VERB
cana-1051	43	14	information	information	NOUN
cana-1051	43	15	using	use	VERB
cana-1051	43	16	a	a	DET
cana-1051	43	17	approximate	approximate	ADJ
cana-1051	43	18	approach	approach	NOUN
cana-1051	43	19	to	to	ADP
cana-1051	43	20	computation	computation	NOUN
cana-1051	43	21	.	.	PUNCT
cana-1051	44	1	in	in	ADP
cana-1051	44	2	this	this	DET
cana-1051	44	3	paper	paper	NOUN
cana-1051	44	4	we	we	PRON
cana-1051	44	5	develop	develop	VERB
cana-1051	44	6	a	a	DET
cana-1051	44	7	feed	feed	NOUN
cana-1051	44	8	forward	forward	ADV
cana-1051	44	9	neural	neural	ADJ
cana-1051	44	10	networks	network	NOUN
cana-1051	44	11	(	(	PUNCT
cana-1051	44	12	ffnn	ffnn	NOUN
cana-1051	44	13	)	)	PUNCT
cana-1051	44	14	model	model	NOUN
cana-1051	44	15	for	for	ADP
cana-1051	44	16	fore	fore	NOUN
cana-1051	44	17	casting	cast	VERB
cana-1051	44	18	exchange	exchange	NOUN
cana-1051	44	19	rates	rate	NOUN
cana-1051	44	20	.	.	PUNCT
cana-1051	45	1	feed	feed	VERB
cana-1051	45	2	forward	forward	ADV
cana-1051	45	3	neural	neural	ADJ
cana-1051	45	4	network	network	NOUN
cana-1051	45	5	(	(	PUNCT
cana-1051	45	6	ffnn	ffnn	NOUN
cana-1051	45	7	)	)	PUNCT
cana-1051	45	8	structure	structure	NOUN
cana-1051	45	9	is	be	AUX
cana-1051	45	10	a	a	DET
cana-1051	45	11	three	three	NUM
cana-1051	45	12	layer	layer	NOUN
cana-1051	45	13	network	network	NOUN
cana-1051	45	14	and	and	CCONJ
cana-1051	45	15	it	it	PRON
cana-1051	45	16	consists	consist	VERB
cana-1051	45	17	of	of	ADP
cana-1051	45	18	an	an	DET
cana-1051	45	19	input	input	NOUN
cana-1051	45	20	layer	layer	NOUN
cana-1051	45	21	,	,	PUNCT
cana-1051	45	22	a	a	DET
cana-1051	45	23	hidden	hidden	ADJ
cana-1051	45	24	layer	layer	NOUN
cana-1051	45	25	and	and	CCONJ
cana-1051	45	26	an	an	DET
cana-1051	45	27	output	output	NOUN
cana-1051	45	28	layer	layer	NOUN
cana-1051	45	29	.	.	PUNCT
cana-1051	46	1	total	total	ADJ
cana-1051	46	2	number	number	NOUN
cana-1051	46	3	of	of	ADP
cana-1051	46	4	input	input	NOUN
cana-1051	46	5	neurons	neuron	NOUN
cana-1051	46	6	needed	need	VERB
cana-1051	46	7	in	in	ADP
cana-1051	46	8	this	this	DET
cana-1051	46	9	model	model	NOUN
cana-1051	46	10	is	be	AUX
cana-1051	46	11	one	one	NUM
cana-1051	46	12	,	,	PUNCT
cana-1051	46	13	and	and	CCONJ
cana-1051	46	14	it	it	PRON
cana-1051	46	15	representing	represent	VERB
cana-1051	46	16	the	the	DET
cana-1051	46	17	values	value	NOUN
cana-1051	46	18	of	of	ADP
cana-1051	46	19	lag1	lag1	NOUN
cana-1051	46	20	(	(	PUNCT
cana-1051	46	21	previous	previous	ADJ
cana-1051	46	22	day	day	NOUN
cana-1051	46	23	exchange	exchange	NOUN
cana-1051	46	24	rate	rate	NOUN
cana-1051	46	25	)	)	PUNCT
cana-1051	46	26	.	.	PUNCT
cana-1051	47	1	in	in	ADP
cana-1051	47	2	this	this	DET
cana-1051	47	3	model	model	NOUN
cana-1051	47	4	only	only	ADV
cana-1051	47	5	one	one	NUM
cana-1051	47	6	output	output	NOUN
cana-1051	47	7	unit	unit	NOUN
cana-1051	47	8	is	be	AUX
cana-1051	47	9	needed	need	VERB
cana-1051	47	10	and	and	CCONJ
cana-1051	47	11	it	it	PRON
cana-1051	47	12	indicates	indicate	VERB
cana-1051	47	13	the	the	DET
cana-1051	47	14	forecasts	forecast	NOUN
cana-1051	47	15	of	of	ADP
cana-1051	47	16	exchange	exchange	NOUN
cana-1051	47	17	rate	rate	NOUN
cana-1051	47	18	for	for	ADP
cana-1051	47	19	inr	inr	PROPN
cana-1051	47	20	-	-	PUNCT
cana-1051	47	21	usd	usd	NOUN
cana-1051	47	22	.	.	PUNCT
cana-1051	48	1	the	the	DET
cana-1051	48	2	following	follow	VERB
cana-1051	48	3	table	table	NOUN
cana-1051	48	4	displays	display	VERB
cana-1051	48	5	information	information	NOUN
cana-1051	48	6	about	about	ADP
cana-1051	48	7	the	the	DET
cana-1051	48	8	neural	neural	ADJ
cana-1051	48	9	networks	network	NOUN
cana-1051	48	10	model	model	NOUN
cana-1051	48	11	,	,	PUNCT
cana-1051	48	12	including	include	VERB
cana-1051	48	13	the	the	DET
cana-1051	48	14	dependent	dependent	ADJ
cana-1051	48	15	variable	variable	NOUN
cana-1051	48	16	,	,	PUNCT
cana-1051	48	17	number	number	NOUN
cana-1051	48	18	of	of	ADP
cana-1051	48	19	input	input	NOUN
cana-1051	48	20	and	and	CCONJ
cana-1051	48	21	output	output	NOUN
cana-1051	48	22	units	unit	NOUN
cana-1051	48	23	,	,	PUNCT
cana-1051	48	24	rescaling	rescaling	NOUN
cana-1051	48	25	method	method	NOUN
cana-1051	48	26	,	,	PUNCT
cana-1051	48	27	number	number	NOUN
cana-1051	48	28	of	of	ADP
cana-1051	48	29	hidden	hidden	ADJ
cana-1051	48	30	layers	layer	NOUN
cana-1051	48	31	and	and	CCONJ
cana-1051	48	32	units	unit	NOUN
cana-1051	48	33	and	and	CCONJ
cana-1051	48	34	activation	activation	NOUN
cana-1051	48	35	functions	function	NOUN
cana-1051	48	36	.	.	PUNCT
cana-1051	49	1	communications	communication	NOUN
cana-1051	49	2	on	on	ADP
cana-1051	49	3	applied	apply	VERB
cana-1051	49	4	nonlinear	nonlinear	ADJ
cana-1051	49	5	analysis	analysis	NOUN
cana-1051	49	6	issn	issn	NOUN
cana-1051	49	7	:	:	PUNCT
cana-1051	49	8	1074	1074	NUM
cana-1051	49	9	-	-	PUNCT
cana-1051	49	10	133x	133x	NUM
cana-1051	49	11	vol	vol	NOUN
cana-1051	49	12	31	31	NUM
cana-1051	49	13	no	no	NOUN
cana-1051	49	14	.	.	PUNCT
cana-1051	50	1	5s	5s	NUM
cana-1051	50	2	(	(	PUNCT
cana-1051	50	3	2024	2024	NUM
cana-1051	50	4	)	)	PUNCT
cana-1051	50	5	303	303	NUM
cana-1051	50	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	50	7	network	network	NOUN
cana-1051	50	8	information	information	NOUN
cana-1051	50	9	input	input	NOUN
cana-1051	50	10	layer	layer	NOUN
cana-1051	50	11	covariates	covariate	VERB
cana-1051	50	12	1	1	NUM
cana-1051	50	13	lag1	lag1	NOUN
cana-1051	50	14	number	number	NOUN
cana-1051	50	15	of	of	ADP
cana-1051	50	16	units	unit	NOUN
cana-1051	50	17	a	a	DET
cana-1051	50	18	1	1	NUM
cana-1051	50	19	rescaling	rescaling	NOUN
cana-1051	50	20	method	method	NOUN
cana-1051	50	21	of	of	ADP
cana-1051	50	22	covariates	covariate	NOUN
cana-1051	50	23	standardized	standardize	VERB
cana-1051	50	24	hidden	hide	VERB
cana-1051	50	25	layer(s	layer(s	NOUN
cana-1051	50	26	)	)	PUNCT
cana-1051	50	27	number	number	NOUN
cana-1051	50	28	of	of	ADP
cana-1051	50	29	hidden	hidden	ADJ
cana-1051	50	30	layers	layer	NOUN
cana-1051	50	31	1	1	NUM
cana-1051	50	32	number	number	NOUN
cana-1051	50	33	of	of	ADP
cana-1051	50	34	units	unit	NOUN
cana-1051	50	35	in	in	ADP
cana-1051	50	36	hidden	hide	VERB
cana-1051	50	37	layer	layer	NOUN
cana-1051	50	38	1a	1a	NOUN
cana-1051	50	39	3	3	NUM
cana-1051	50	40	activation	activation	NOUN
cana-1051	50	41	function	function	VERB
cana-1051	50	42	hyperbolic	hyperbolic	ADJ
cana-1051	50	43	tangent	tangent	NOUN
cana-1051	50	44	output	output	NOUN
cana-1051	50	45	layer	layer	NOUN
cana-1051	50	46	dependent	dependent	ADJ
cana-1051	50	47	variables	variable	VERB
cana-1051	50	48	1	1	NUM
cana-1051	50	49	inr	inr	PROPN
cana-1051	50	50	-	-	PUNCT
cana-1051	50	51	usd	usd	NOUN
cana-1051	50	52	number	number	NOUN
cana-1051	50	53	of	of	ADP
cana-1051	50	54	units	unit	NOUN
cana-1051	50	55	1	1	NUM
cana-1051	50	56	rescaling	rescaling	NOUN
cana-1051	50	57	method	method	NOUN
cana-1051	50	58	for	for	ADP
cana-1051	50	59	scale	scale	NOUN
cana-1051	50	60	dependents	dependent	NOUN
cana-1051	50	61	.	.	PUNCT
cana-1051	51	1	standardized	standardized	ADJ
cana-1051	51	2	activation	activation	NOUN
cana-1051	51	3	function	function	NOUN
cana-1051	51	4	identity	identity	NOUN
cana-1051	51	5	error	error	NOUN
cana-1051	51	6	function	function	NOUN
cana-1051	51	7	sum	sum	NOUN
cana-1051	51	8	of	of	ADP
cana-1051	51	9	squares	square	NOUN
cana-1051	51	10	a.	a.	NOUN
cana-1051	51	11	excluding	exclude	VERB
cana-1051	51	12	the	the	DET
cana-1051	51	13	bias	bias	NOUN
cana-1051	51	14	unit	unit	NOUN
cana-1051	51	15	.	.	PUNCT
cana-1051	52	1	back	back	ADJ
cana-1051	52	2	propagation	propagation	NOUN
cana-1051	52	3	theorem	theorem	NOUN
cana-1051	52	4	is	be	AUX
cana-1051	52	5	used	use	VERB
cana-1051	52	6	in	in	ADP
cana-1051	52	7	learning	learning	NOUN
cana-1051	52	8	of	of	ADP
cana-1051	52	9	the	the	DET
cana-1051	52	10	network	network	NOUN
cana-1051	52	11	.	.	PUNCT
cana-1051	53	1	the	the	DET
cana-1051	53	2	network	network	NOUN
cana-1051	53	3	is	be	AUX
cana-1051	53	4	trained	train	VERB
cana-1051	53	5	using	use	VERB
cana-1051	53	6	back	back	ADP
cana-1051	53	7	propagation	propagation	NOUN
cana-1051	53	8	algorithm	algorithm	NOUN
cana-1051	53	9	until	until	SCONJ
cana-1051	53	10	the	the	DET
cana-1051	53	11	sum	sum	NOUN
cana-1051	53	12	of	of	ADP
cana-1051	53	13	squares	square	NOUN
cana-1051	53	14	of	of	ADP
cana-1051	53	15	error	error	NOUN
cana-1051	53	16	is	be	AUX
cana-1051	53	17	small	small	ADJ
cana-1051	53	18	for	for	ADP
cana-1051	53	19	the	the	DET
cana-1051	53	20	training	training	NOUN
cana-1051	53	21	set	set	NOUN
cana-1051	53	22	.	.	PUNCT
cana-1051	54	1	haykin	haykin	PROPN
cana-1051	54	2	,	,	PUNCT
cana-1051	54	3	1999	1999	NUM
cana-1051	54	4	,	,	PUNCT
cana-1051	54	5	rama	rama	PROPN
cana-1051	54	6	krishna	krishna	PROPN
cana-1051	54	7	et	et	PROPN
cana-1051	54	8	al	al	PROPN
cana-1051	54	9	.	.	PROPN
cana-1051	54	10	,	,	PUNCT
cana-1051	54	11	2013	2013	NUM
cana-1051	54	12	,	,	PUNCT
cana-1051	54	13	[	[	X
cana-1051	54	14	1,11	1,11	X
cana-1051	54	15	]	]	X
cana-1051	54	16	.	.	PUNCT
cana-1051	55	1	2.3	2.3	NUM
cana-1051	55	2	testing	testing	NOUN
cana-1051	55	3	equality	equality	NOUN
cana-1051	55	4	of	of	ADP
cana-1051	55	5	forecasting	forecasting	NOUN
cana-1051	55	6	performance	performance	NOUN
cana-1051	55	7	of	of	ADP
cana-1051	55	8	the	the	DET
cana-1051	55	9	models	model	NOUN
cana-1051	55	10	to	to	PART
cana-1051	55	11	compare	compare	VERB
cana-1051	55	12	the	the	DET
cana-1051	55	13	multiple	multiple	ADJ
cana-1051	55	14	forecasting	forecasting	NOUN
cana-1051	55	15	models	model	NOUN
cana-1051	55	16	with	with	ADP
cana-1051	55	17	respect	respect	NOUN
cana-1051	55	18	to	to	ADP
cana-1051	55	19	absolute	absolute	ADJ
cana-1051	55	20	errors	error	NOUN
cana-1051	55	21	friedman	friedman	PROPN
cana-1051	55	22	’s	’s	PART
cana-1051	55	23	test	test	NOUN
cana-1051	55	24	is	be	AUX
cana-1051	55	25	used	use	VERB
cana-1051	55	26	.	.	PUNCT
cana-1051	56	1	under	under	ADP
cana-1051	56	2	the	the	DET
cana-1051	56	3	null	null	ADJ
cana-1051	56	4	hypothesis	hypothesis	NOUN
cana-1051	56	5	that	that	PRON
cana-1051	56	6	all	all	DET
cana-1051	56	7	models	model	NOUN
cana-1051	56	8	are	be	AUX
cana-1051	56	9	equivalent	equivalent	ADJ
cana-1051	56	10	in	in	ADP
cana-1051	56	11	performance	performance	NOUN
cana-1051	56	12	the	the	DET
cana-1051	56	13	friedman	friedman	PROPN
cana-1051	56	14	’s	’s	PART
cana-1051	56	15	test	test	NOUN
cana-1051	56	16	statistics	statistic	NOUN
cana-1051	56	17	is	be	AUX
cana-1051	56	18	given	give	VERB
cana-1051	56	19	by	by	ADP
cana-1051	56	20	𝜒𝐹	𝜒𝐹	PROPN
cana-1051	56	21	2	2	NUM
cana-1051	56	22	=	=	SYM
cana-1051	56	23	12	12	NUM
cana-1051	56	24	𝑛𝑘(𝑘+1	𝑛𝑘(𝑘+1	NOUN
cana-1051	56	25	)	)	PUNCT
cana-1051	56	26	∑	∑	PUNCT
cana-1051	56	27	𝑅𝑗	𝑅𝑗	ADP
cana-1051	56	28	2𝑘	2𝑘	NUM
cana-1051	56	29	𝑗=1	𝑗=1	NOUN
cana-1051	56	30	3n	3n	NUM
cana-1051	56	31	(	(	PUNCT
cana-1051	56	32	k+1	k+1	NOUN
cana-1051	56	33	)	)	PUNCT
cana-1051	56	34	is	be	AUX
cana-1051	56	35	approximately	approximately	ADV
cana-1051	56	36	distributed	distribute	VERB
cana-1051	56	37	as	as	ADP
cana-1051	56	38	𝜒2	𝜒2	NOUN
cana-1051	56	39	with	with	ADP
cana-1051	56	40	k-1	k-1	PROPN
cana-1051	56	41	degrees	degree	NOUN
cana-1051	56	42	of	of	ADP
cana-1051	56	43	freedom	freedom	NOUN
cana-1051	56	44	and	and	CCONJ
cana-1051	56	45	where	where	SCONJ
cana-1051	56	46	k=1	k=1	PROPN
cana-1051	56	47	number	number	NOUN
cana-1051	56	48	of	of	ADP
cana-1051	56	49	models	model	NOUN
cana-1051	56	50	,	,	PUNCT
cana-1051	56	51	n	n	NOUN
cana-1051	56	52	=	=	NOUN
cana-1051	56	53	number	number	NOUN
cana-1051	56	54	observations	observation	NOUN
cana-1051	56	55	in	in	ADP
cana-1051	56	56	each	each	DET
cana-1051	56	57	model	model	NOUN
cana-1051	56	58	.	.	PUNCT
cana-1051	57	1	the	the	DET
cana-1051	57	2	data	datum	NOUN
cana-1051	57	3	are	be	AUX
cana-1051	57	4	daily	daily	ADJ
cana-1051	57	5	fedai	fedai	NOUN
cana-1051	57	6	indicative	indicative	ADJ
cana-1051	57	7	from	from	ADP
cana-1051	57	8	1st	1st	PROPN
cana-1051	57	9	january,2019	january,2019	PROPN
cana-1051	57	10	to	to	ADP
cana-1051	57	11	23rd	23rd	ADJ
cana-1051	57	12	october	october	PROPN
cana-1051	57	13	,	,	PUNCT
cana-1051	57	14	2023	2023	NUM
cana-1051	57	15	from	from	ADP
cana-1051	57	16	http://dbie.rbi.org.in	http://dbie.rbi.org.in	NOUN
cana-1051	57	17	and	and	CCONJ
cana-1051	57	18	the	the	DET
cana-1051	57	19	same	same	ADJ
cana-1051	57	20	is	be	AUX
cana-1051	57	21	divided	divide	VERB
cana-1051	57	22	into	into	ADP
cana-1051	57	23	training	training	NOUN
cana-1051	57	24	sample	sample	NOUN
cana-1051	57	25	(	(	PUNCT
cana-1051	57	26	till	till	SCONJ
cana-1051	57	27	29th	29th	ADJ
cana-1051	57	28	september	september	PROPN
cana-1051	57	29	,	,	PUNCT
cana-1051	57	30	2023	2023	NUM
cana-1051	57	31	)	)	PUNCT
cana-1051	57	32	and	and	CCONJ
cana-1051	57	33	outof	outof	PROPN
cana-1051	57	34	-	-	PUNCT
cana-1051	57	35	sample	sample	NOUN
cana-1051	57	36	(	(	PUNCT
cana-1051	57	37	from	from	ADP
cana-1051	57	38	3rd	3rd	PROPN
cana-1051	57	39	april	april	PROPN
cana-1051	57	40	,	,	PUNCT
cana-1051	57	41	2023	2023	NUM
cana-1051	57	42	to	to	ADP
cana-1051	57	43	23rd	23rd	ADJ
cana-1051	57	44	october	october	PROPN
cana-1051	57	45	,	,	PUNCT
cana-1051	57	46	2023	2023	NUM
cana-1051	57	47	)	)	PUNCT
cana-1051	57	48	.	.	PUNCT
cana-1051	58	1	the	the	DET
cana-1051	58	2	arima	arima	PROPN
cana-1051	58	3	and	and	CCONJ
cana-1051	58	4	ffnn	ffnn	NOUN
cana-1051	58	5	models	model	NOUN
cana-1051	58	6	fitted	fit	VERB
cana-1051	58	7	to	to	ADP
cana-1051	58	8	the	the	DET
cana-1051	58	9	training	training	NOUN
cana-1051	58	10	sample	sample	NOUN
cana-1051	58	11	and	and	CCONJ
cana-1051	58	12	validated	validate	VERB
cana-1051	58	13	on	on	ADP
cana-1051	58	14	the	the	DET
cana-1051	58	15	out	out	NOUN
cana-1051	58	16	of	of	ADP
cana-1051	58	17	sample	sample	NOUN
cana-1051	58	18	.	.	PUNCT
cana-1051	59	1	the	the	DET
cana-1051	59	2	actual	actual	ADJ
cana-1051	59	3	time	time	NOUN
cana-1051	59	4	series	series	PROPN
cana-1051	59	5	data	datum	NOUN
cana-1051	59	6	was	be	AUX
cana-1051	59	7	plotted	plot	VERB
cana-1051	59	8	in	in	ADP
cana-1051	59	9	figure1	figure1	PROPN
cana-1051	60	1	and	and	CCONJ
cana-1051	60	2	it	it	PRON
cana-1051	60	3	shows	show	VERB
cana-1051	60	4	more	more	ADJ
cana-1051	60	5	fluctuations	fluctuation	NOUN
cana-1051	60	6	in	in	ADP
cana-1051	60	7	the	the	DET
cana-1051	60	8	inr	inr	PROPN
cana-1051	60	9	-	-	PUNCT
cana-1051	60	10	usd	usd	NOUN
cana-1051	60	11	exchange	exchange	NOUN
cana-1051	60	12	rates	rate	NOUN
cana-1051	60	13	over	over	ADP
cana-1051	60	14	a	a	DET
cana-1051	60	15	period	period	NOUN
cana-1051	60	16	of	of	ADP
cana-1051	60	17	time	time	NOUN
cana-1051	60	18	.	.	PUNCT
cana-1051	61	1	3	3	X
cana-1051	61	2	.	.	X
cana-1051	61	3	building	build	VERB
cana-1051	61	4	arima	arima	PROPN
cana-1051	61	5	model	model	NOUN
cana-1051	61	6	the	the	DET
cana-1051	61	7	development	development	NOUN
cana-1051	61	8	of	of	ADP
cana-1051	61	9	arima	arima	PROPN
cana-1051	61	10	model	model	NOUN
cana-1051	61	11	for	for	ADP
cana-1051	61	12	any	any	DET
cana-1051	61	13	variable	variable	NOUN
cana-1051	61	14	involves	involve	VERB
cana-1051	61	15	identification	identification	NOUN
cana-1051	61	16	,	,	PUNCT
cana-1051	61	17	estimation	estimation	NOUN
cana-1051	61	18	,	,	PUNCT
cana-1051	61	19	diagnostics	diagnostic	NOUN
cana-1051	61	20	checking	check	VERB
cana-1051	61	21	and	and	CCONJ
cana-1051	61	22	forecasting	forecasting	NOUN
cana-1051	61	23	.	.	PUNCT
cana-1051	62	1	the	the	DET
cana-1051	62	2	time	time	NOUN
cana-1051	62	3	plot	plot	NOUN
cana-1051	62	4	of	of	ADP
cana-1051	62	5	daily	daily	ADJ
cana-1051	62	6	exchange	exchange	NOUN
cana-1051	62	7	rates	rate	NOUN
cana-1051	62	8	from	from	ADP
cana-1051	62	9	1st	1st	ADJ
cana-1051	62	10	january	january	PROPN
cana-1051	62	11	,	,	PUNCT
cana-1051	62	12	2019	2019	NUM
cana-1051	62	13	to	to	ADP
cana-1051	62	14	23rd	23rd	ADJ
cana-1051	62	15	october	october	NOUN
cana-1051	62	16	2023	2023	NUM
cana-1051	62	17	is	be	AUX
cana-1051	62	18	given	give	VERB
cana-1051	62	19	in	in	ADP
cana-1051	62	20	figure1	figure1	PROPN
cana-1051	62	21	.	.	PUNCT
cana-1051	63	1	communications	communication	NOUN
cana-1051	63	2	on	on	ADP
cana-1051	63	3	applied	apply	VERB
cana-1051	63	4	nonlinear	nonlinear	ADJ
cana-1051	63	5	analysis	analysis	NOUN
cana-1051	63	6	issn	issn	NOUN
cana-1051	63	7	:	:	PUNCT
cana-1051	63	8	1074	1074	NUM
cana-1051	63	9	-	-	PUNCT
cana-1051	63	10	133x	133x	NUM
cana-1051	63	11	vol	vol	NOUN
cana-1051	63	12	31	31	NUM
cana-1051	63	13	no	no	NOUN
cana-1051	63	14	.	.	PUNCT
cana-1051	64	1	5s	5s	NUM
cana-1051	64	2	(	(	PUNCT
cana-1051	64	3	2024	2024	NUM
cana-1051	64	4	)	)	PUNCT
cana-1051	64	5	304	304	NUM
cana-1051	64	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	64	7	figure-1	figure-1	PROPN
cana-1051	64	8	:	:	PUNCT
cana-1051	64	9	time	time	NOUN
cana-1051	64	10	plot	plot	NOUN
cana-1051	64	11	of	of	ADP
cana-1051	64	12	daily	daily	ADJ
cana-1051	64	13	exchange	exchange	NOUN
cana-1051	64	14	rates	rate	NOUN
cana-1051	64	15	.	.	PUNCT
cana-1051	65	1	the	the	DET
cana-1051	65	2	sample	sample	NOUN
cana-1051	65	3	autocorrelation	autocorrelation	NOUN
cana-1051	65	4	function	function	NOUN
cana-1051	65	5	is	be	AUX
cana-1051	65	6	computed	compute	VERB
cana-1051	65	7	to	to	PART
cana-1051	65	8	check	check	VERB
cana-1051	65	9	whether	whether	SCONJ
cana-1051	65	10	the	the	DET
cana-1051	65	11	series	series	NOUN
cana-1051	65	12	is	be	AUX
cana-1051	65	13	stationary	stationary	ADJ
cana-1051	65	14	or	or	CCONJ
cana-1051	65	15	nonstationary	nonstationary	ADJ
cana-1051	65	16	.	.	PUNCT
cana-1051	66	1	the	the	DET
cana-1051	66	2	sample	sample	NOUN
cana-1051	66	3	acf	acf	NOUN
cana-1051	66	4	for	for	ADP
cana-1051	66	5	50	50	NUM
cana-1051	66	6	lags	lag	NOUN
cana-1051	66	7	is	be	AUX
cana-1051	66	8	given	give	VERB
cana-1051	66	9	in	in	ADP
cana-1051	66	10	figure-2	figure-2	PROPN
cana-1051	66	11	.	.	PUNCT
cana-1051	67	1	figure-2	figure-2	NUM
cana-1051	67	2	:	:	PUNCT
cana-1051	67	3	auto	auto	NOUN
cana-1051	67	4	correlation	correlation	NOUN
cana-1051	67	5	function	function	NOUN
cana-1051	67	6	figure-3	figure-3	NUM
cana-1051	67	7	:	:	PUNCT
cana-1051	67	8	partial	partial	ADJ
cana-1051	67	9	auto	auto	NOUN
cana-1051	67	10	correlation	correlation	NOUN
cana-1051	67	11	function	function	NOUN
cana-1051	67	12	from	from	ADP
cana-1051	67	13	the	the	DET
cana-1051	67	14	figure-2	figure-2	PROPN
cana-1051	67	15	&	&	CCONJ
cana-1051	67	16	3	3	NUM
cana-1051	67	17	,	,	PUNCT
cana-1051	67	18	it	it	PRON
cana-1051	67	19	is	be	AUX
cana-1051	67	20	observed	observe	VERB
cana-1051	67	21	that	that	SCONJ
cana-1051	67	22	the	the	DET
cana-1051	67	23	time	time	NOUN
cana-1051	67	24	plot	plot	NOUN
cana-1051	67	25	as	as	ADP
cana-1051	67	26	a	a	DET
cana-1051	67	27	downward	downward	ADJ
cana-1051	67	28	trend	trend	NOUN
cana-1051	67	29	and	and	CCONJ
cana-1051	67	30	acf	acf	PROPN
cana-1051	67	31	dies	die	VERB
cana-1051	67	32	out	out	ADP
cana-1051	67	33	slowly	slowly	ADV
cana-1051	67	34	for	for	ADP
cana-1051	67	35	higher	high	ADJ
cana-1051	67	36	lags	lag	NOUN
cana-1051	67	37	,	,	PUNCT
cana-1051	67	38	this	this	PRON
cana-1051	67	39	indicates	indicate	VERB
cana-1051	67	40	the	the	DET
cana-1051	67	41	time	time	NOUN
cana-1051	67	42	series	series	NOUN
cana-1051	67	43	is	be	AUX
cana-1051	67	44	not	not	PART
cana-1051	67	45	stationary	stationary	ADJ
cana-1051	67	46	.	.	PUNCT
cana-1051	68	1	arima	arima	PROPN
cana-1051	68	2	model	model	NOUN
cana-1051	68	3	is	be	AUX
cana-1051	68	4	estimated	estimate	VERB
cana-1051	68	5	only	only	ADV
cana-1051	68	6	after	after	ADP
cana-1051	68	7	transforming	transform	VERB
cana-1051	68	8	the	the	DET
cana-1051	68	9	variable	variable	NOUN
cana-1051	68	10	under	under	ADP
cana-1051	68	11	forecasting	forecast	VERB
cana-1051	68	12	into	into	ADP
cana-1051	68	13	a	a	DET
cana-1051	68	14	stationary	stationary	ADJ
cana-1051	68	15	series	series	NOUN
cana-1051	68	16	.	.	PUNCT
cana-1051	69	1	non	non	PROPN
cana-1051	69	2	stationarity	stationarity	PROPN
cana-1051	69	3	in	in	ADP
cana-1051	69	4	variance	variance	NOUN
cana-1051	69	5	is	be	AUX
cana-1051	69	6	corrected	correct	VERB
cana-1051	69	7	through	through	ADP
cana-1051	69	8	natural	natural	ADJ
cana-1051	69	9	log	log	NOUN
cana-1051	69	10	transformation	transformation	NOUN
cana-1051	69	11	and	and	CCONJ
cana-1051	69	12	non	non	ADJ
cana-1051	69	13	-	-	NOUN
cana-1051	69	14	stationarity	stationarity	NOUN
cana-1051	69	15	in	in	ADP
cana-1051	69	16	mean	mean	PROPN
cana-1051	69	17	is	be	AUX
cana-1051	69	18	corrected	correct	VERB
cana-1051	69	19	through	through	ADP
cana-1051	69	20	appropriate	appropriate	ADJ
cana-1051	69	21	differencing	differencing	NOUN
cana-1051	69	22	of	of	ADP
cana-1051	69	23	the	the	DET
cana-1051	69	24	data	datum	NOUN
cana-1051	69	25	.	.	PUNCT
cana-1051	70	1	the	the	DET
cana-1051	70	2	time	time	NOUN
cana-1051	70	3	plot	plot	NOUN
cana-1051	70	4	of	of	ADP
cana-1051	70	5	transformed	transform	VERB
cana-1051	70	6	series	series	NOUN
cana-1051	70	7	is	be	AUX
cana-1051	70	8	given	give	VERB
cana-1051	70	9	in	in	ADP
cana-1051	70	10	figure-4	figure-4	NOUN
cana-1051	70	11	.	.	PUNCT
cana-1051	71	1	figure-4	figure-4	NUM
cana-1051	71	2	:	:	PUNCT
cana-1051	71	3	time	time	NOUN
cana-1051	71	4	plot	plot	NOUN
cana-1051	71	5	of	of	ADP
cana-1051	71	6	transformed	transform	VERB
cana-1051	71	7	series	series	NOUN
cana-1051	71	8	communications	communication	NOUN
cana-1051	71	9	on	on	ADP
cana-1051	71	10	applied	apply	VERB
cana-1051	71	11	nonlinear	nonlinear	ADJ
cana-1051	71	12	analysis	analysis	NOUN
cana-1051	71	13	issn	issn	NOUN
cana-1051	71	14	:	:	PUNCT
cana-1051	71	15	1074	1074	NUM
cana-1051	71	16	-	-	PUNCT
cana-1051	71	17	133x	133x	NUM
cana-1051	71	18	vol	vol	NOUN
cana-1051	71	19	31	31	NUM
cana-1051	71	20	no	no	NOUN
cana-1051	71	21	.	.	PUNCT
cana-1051	72	1	5s	5s	NUM
cana-1051	72	2	(	(	PUNCT
cana-1051	72	3	2024	2024	NUM
cana-1051	72	4	)	)	PUNCT
cana-1051	72	5	305	305	NUM
cana-1051	72	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	72	7	in	in	ADP
cana-1051	72	8	this	this	DET
cana-1051	72	9	case	case	NOUN
cana-1051	72	10	is	be	AUX
cana-1051	72	11	observed	observe	VERB
cana-1051	72	12	that	that	SCONJ
cana-1051	72	13	difference	difference	NOUN
cana-1051	72	14	of	of	ADP
cana-1051	72	15	order	order	NOUN
cana-1051	72	16	1	1	NUM
cana-1051	72	17	(	(	PUNCT
cana-1051	72	18	d=1	d=1	NOUN
cana-1051	72	19	)	)	PUNCT
cana-1051	72	20	is	be	AUX
cana-1051	72	21	sufficient	sufficient	ADJ
cana-1051	72	22	to	to	PART
cana-1051	72	23	achieve	achieve	VERB
cana-1051	72	24	stationary	stationary	NOUN
cana-1051	72	25	in	in	ADP
cana-1051	72	26	mean	mean	PROPN
cana-1051	72	27	the	the	DET
cana-1051	72	28	newly	newly	ADV
cana-1051	72	29	constructed	construct	VERB
cana-1051	72	30	variable	variable	NOUN
cana-1051	72	31	𝑊𝑡	𝑊𝑡	PROPN
cana-1051	72	32	=	=	SYM
cana-1051	72	33	∇1	∇1	PROPN
cana-1051	72	34	ln(𝑍𝑡	ln(𝑍𝑡	PROPN
cana-1051	72	35	)	)	PUNCT
cana-1051	72	36	can	can	AUX
cana-1051	72	37	now	now	ADV
cana-1051	72	38	be	be	AUX
cana-1051	72	39	examined	examine	VERB
cana-1051	72	40	for	for	ADP
cana-1051	72	41	stationarity	stationarity	NOUN
cana-1051	72	42	and	and	CCONJ
cana-1051	72	43	it	it	PRON
cana-1051	72	44	is	be	AUX
cana-1051	72	45	observed	observe	VERB
cana-1051	72	46	that	that	SCONJ
cana-1051	72	47	𝑊𝑡	𝑊𝑡	PROPN
cana-1051	72	48	is	be	AUX
cana-1051	72	49	stationary	stationary	ADJ
cana-1051	72	50	in	in	ADP
cana-1051	72	51	mean	mean	NOUN
cana-1051	72	52	and	and	CCONJ
cana-1051	72	53	variance	variance	NOUN
cana-1051	72	54	.	.	PUNCT
cana-1051	73	1	the	the	DET
cana-1051	73	2	next	next	ADJ
cana-1051	73	3	step	step	NOUN
cana-1051	73	4	is	be	AUX
cana-1051	73	5	to	to	ADP
cana-1051	73	6	identifying	identify	VERB
cana-1051	73	7	the	the	DET
cana-1051	73	8	values	value	NOUN
cana-1051	73	9	of	of	ADP
cana-1051	73	10	𝑝	𝑝	NOUN
cana-1051	73	11	𝑎𝑛𝑑	𝑎𝑛𝑑	ADJ
cana-1051	73	12	𝑞	𝑞	PROPN
cana-1051	73	13	for	for	ADP
cana-1051	73	14	autocorrelations	autocorrelation	NOUN
cana-1051	73	15	and	and	CCONJ
cana-1051	73	16	partial	partial	ADJ
cana-1051	73	17	auto	auto	NOUN
cana-1051	73	18	correlations	correlation	NOUN
cana-1051	73	19	of	of	ADP
cana-1051	73	20	various	various	ADJ
cana-1051	73	21	orders	order	NOUN
cana-1051	73	22	𝑊𝑡	𝑊𝑡	PROPN
cana-1051	73	23	are	be	AUX
cana-1051	73	24	computed	compute	VERB
cana-1051	73	25	.	.	PUNCT
cana-1051	74	1	figure-5	figure-5	NUM
cana-1051	74	2	:	:	PUNCT
cana-1051	74	3	acf	acf	NOUN
cana-1051	74	4	for	for	ADP
cana-1051	74	5	transformed	transform	VERB
cana-1051	74	6	series	series	NOUN
cana-1051	74	7	.	.	PUNCT
cana-1051	75	1	figure-6	figure-6	ADV
cana-1051	75	2	:	:	PUNCT
cana-1051	75	3	pacf	pacf	NOUN
cana-1051	75	4	for	for	ADP
cana-1051	75	5	transformed	transform	VERB
cana-1051	75	6	series	series	NOUN
cana-1051	75	7	from	from	ADP
cana-1051	75	8	the	the	DET
cana-1051	75	9	above	above	ADJ
cana-1051	75	10	acf	acf	NOUN
cana-1051	75	11	and	and	CCONJ
cana-1051	75	12	pacf	pacf	PROPN
cana-1051	75	13	and	and	CCONJ
cana-1051	75	14	the	the	DET
cana-1051	75	15	spss	spss	ADJ
cana-1051	75	16	skilled	skilled	ADJ
cana-1051	75	17	creator	creator	NOUN
cana-1051	75	18	was	be	AUX
cana-1051	75	19	accustomed	accustom	VERB
cana-1051	75	20	to	to	PART
cana-1051	75	21	determine	determine	VERB
cana-1051	75	22	the	the	DET
cana-1051	75	23	most	most	ADV
cana-1051	75	24	effective	effective	ADJ
cana-1051	75	25	arima	arima	NOUN
cana-1051	75	26	model	model	NOUN
cana-1051	75	27	for	for	ADP
cana-1051	75	28	the	the	DET
cana-1051	75	29	prediction	prediction	NOUN
cana-1051	75	30	of	of	ADP
cana-1051	75	31	usd	usd	NOUN
cana-1051	75	32	exchange	exchange	NOUN
cana-1051	75	33	rates	rate	NOUN
cana-1051	75	34	,	,	PUNCT
cana-1051	75	35	as	as	ADP
cana-1051	75	36	this	this	DET
cana-1051	75	37	plan	plan	NOUN
cana-1051	75	38	to	to	PART
cana-1051	75	39	estimate	estimate	VERB
cana-1051	75	40	the	the	DET
cana-1051	75	41	best	good	ADJ
cana-1051	75	42	–	–	PUNCT
cana-1051	75	43	filtering	filter	VERB
cana-1051	75	44	arima	arima	NOUN
cana-1051	75	45	for	for	ADP
cana-1051	75	46	one	one	NUM
cana-1051	75	47	or	or	CCONJ
cana-1051	75	48	additional	additional	ADJ
cana-1051	75	49	variable	variable	ADJ
cana-1051	75	50	series	series	NOUN
cana-1051	75	51	,	,	PUNCT
cana-1051	75	52	therefore	therefore	ADV
cana-1051	75	53	eliminating	eliminate	VERB
cana-1051	75	54	the	the	DET
cana-1051	75	55	necessity	necessity	NOUN
cana-1051	75	56	to	to	PART
cana-1051	75	57	spot	spot	VERB
cana-1051	75	58	an	an	DET
cana-1051	75	59	applicable	applicable	ADJ
cana-1051	75	60	model	model	NOUN
cana-1051	75	61	through	through	ADP
cana-1051	75	62	trial	trial	NOUN
cana-1051	75	63	and	and	CCONJ
cana-1051	75	64	error	error	NOUN
cana-1051	75	65	methodology	methodology	NOUN
cana-1051	75	66	.	.	PUNCT
cana-1051	76	1	it	it	PRON
cana-1051	76	2	is	be	AUX
cana-1051	76	3	discovered	discover	VERB
cana-1051	76	4	that	that	SCONJ
cana-1051	76	5	,	,	PUNCT
cana-1051	76	6	arima	arima	PROPN
cana-1051	76	7	(	(	PUNCT
cana-1051	76	8	1	1	NUM
cana-1051	76	9	,	,	PUNCT
cana-1051	76	10	1	1	NUM
cana-1051	76	11	,	,	PUNCT
cana-1051	76	12	1	1	X
cana-1051	76	13	)	)	PUNCT
cana-1051	76	14	model	model	NOUN
cana-1051	76	15	fits	fit	VERB
cana-1051	76	16	the	the	DET
cana-1051	76	17	data	datum	NOUN
cana-1051	76	18	well	well	ADV
cana-1051	76	19	and	and	CCONJ
cana-1051	76	20	therefore	therefore	ADV
cana-1051	76	21	the	the	DET
cana-1051	76	22	same	same	ADJ
cana-1051	76	23	is	be	AUX
cana-1051	76	24	tested	test	VERB
cana-1051	76	25	on	on	ADP
cana-1051	76	26	the	the	DET
cana-1051	76	27	validation	validation	NOUN
cana-1051	76	28	set	set	NOUN
cana-1051	76	29	.	.	PUNCT
cana-1051	77	1	table	table	NOUN
cana-1051	77	2	1	1	NUM
cana-1051	77	3	:	:	PUNCT
cana-1051	77	4	arima	arima	NOUN
cana-1051	77	5	model	model	NOUN
cana-1051	77	6	(	(	PUNCT
cana-1051	77	7	1	1	NUM
cana-1051	77	8	,	,	PUNCT
cana-1051	77	9	1	1	NUM
cana-1051	77	10	,	,	PUNCT
cana-1051	77	11	1	1	X
cana-1051	77	12	)	)	PUNCT
cana-1051	77	13	parameter	parameter	NOUN
cana-1051	77	14	estimates	estimate	NOUN
cana-1051	77	15	estimate	estimate	VERB
cana-1051	77	16	se	se	PROPN
cana-1051	77	17	t	t	PROPN
cana-1051	77	18	usdmodel_1	usdmodel_1	PROPN
cana-1051	77	19	usd	usd	NOUN
cana-1051	78	1	no	no	DET
cana-1051	78	2	transformation	transformation	NOUN
cana-1051	78	3	ar	ar	NOUN
cana-1051	78	4	lag	lag	VERB
cana-1051	78	5	1	1	NUM
cana-1051	78	6	-0.993	-0.993	SYM
cana-1051	78	7	0.018	0.018	NUM
cana-1051	78	8	-55.842	-55.842	PUNCT
cana-1051	78	9	difference	difference	NOUN
cana-1051	78	10	1	1	NUM
cana-1051	78	11	ma	ma	PROPN
cana-1051	78	12	lag	lag	PROPN
cana-1051	78	13	1	1	NUM
cana-1051	78	14	-0.990	-0.990	ADP
cana-1051	78	15	0.022	0.022	NUM
cana-1051	78	16	-44.335	-44.335	PUNCT
cana-1051	78	17	from	from	ADP
cana-1051	78	18	the	the	DET
cana-1051	78	19	above	above	ADJ
cana-1051	78	20	table	table	NOUN
cana-1051	78	21	arima	arima	NOUN
cana-1051	78	22	(	(	PUNCT
cana-1051	78	23	1	1	NUM
cana-1051	78	24	,	,	PUNCT
cana-1051	78	25	1	1	NUM
cana-1051	78	26	,	,	PUNCT
cana-1051	78	27	1	1	X
cana-1051	78	28	)	)	PUNCT
cana-1051	78	29	significant	significant	ADJ
cana-1051	78	30	with	with	ADP
cana-1051	78	31	respective	respective	ADJ
cana-1051	78	32	parameters	parameter	NOUN
cana-1051	78	33	as	as	ADV
cana-1051	78	34	well	well	ADV
cana-1051	78	35	as	as	ADP
cana-1051	78	36	adequacy	adequacy	NOUN
cana-1051	78	37	of	of	ADP
cana-1051	78	38	the	the	DET
cana-1051	78	39	model	model	NOUN
cana-1051	78	40	.	.	PUNCT
cana-1051	79	1	hence	hence	ADV
cana-1051	79	2	the	the	DET
cana-1051	79	3	fitted	fit	VERB
cana-1051	79	4	model	model	NOUN
cana-1051	79	5	for	for	ADP
cana-1051	79	6	the	the	DET
cana-1051	79	7	forecasting	forecasting	NOUN
cana-1051	79	8	of	of	ADP
cana-1051	79	9	usd	usd	NOUN
cana-1051	79	10	exchange	exchange	NOUN
cana-1051	79	11	rate	rate	NOUN
cana-1051	79	12	is	be	AUX
cana-1051	79	13	(	(	PUNCT
cana-1051	79	14	1	1	NUM
cana-1051	79	15	+	+	NUM
cana-1051	79	16	0.993b)∇1𝐼𝑛(𝑍𝑡	0.993b)∇1𝐼𝑛(𝑍𝑡	NOUN
cana-1051	79	17	)	)	PUNCT
cana-1051	79	18	=	=	PUNCT
cana-1051	80	1	(	(	PUNCT
cana-1051	80	2	1	1	NUM
cana-1051	80	3	+	+	CCONJ
cana-1051	80	4	0.990𝐵)𝑎𝑡	0.990𝐵)𝑎𝑡	NUM
cana-1051	80	5	diagnostic	diagnostic	ADJ
cana-1051	80	6	checking	checking	NOUN
cana-1051	80	7	is	be	AUX
cana-1051	80	8	done	do	VERB
cana-1051	80	9	through	through	ADP
cana-1051	80	10	examining	examine	VERB
cana-1051	80	11	autocorrelations	autocorrelation	NOUN
cana-1051	80	12	and	and	CCONJ
cana-1051	80	13	partial	partial	ADJ
cana-1051	80	14	autocorrelations	autocorrelation	NOUN
cana-1051	80	15	of	of	ADP
cana-1051	80	16	the	the	DET
cana-1051	80	17	residual	residual	NOUN
cana-1051	80	18	of	of	ADP
cana-1051	80	19	various	various	ADJ
cana-1051	80	20	orders	order	NOUN
cana-1051	80	21	.	.	PUNCT
cana-1051	81	1	communications	communication	NOUN
cana-1051	81	2	on	on	ADP
cana-1051	81	3	applied	apply	VERB
cana-1051	81	4	nonlinear	nonlinear	ADJ
cana-1051	81	5	analysis	analysis	NOUN
cana-1051	81	6	issn	issn	NOUN
cana-1051	81	7	:	:	PUNCT
cana-1051	81	8	1074	1074	NUM
cana-1051	81	9	-	-	PUNCT
cana-1051	81	10	133x	133x	NUM
cana-1051	81	11	vol	vol	NOUN
cana-1051	81	12	31	31	NUM
cana-1051	81	13	no	no	NOUN
cana-1051	81	14	.	.	PUNCT
cana-1051	82	1	5s	5s	NUM
cana-1051	82	2	(	(	PUNCT
cana-1051	82	3	2024	2024	NUM
cana-1051	82	4	)	)	PUNCT
cana-1051	82	5	306	306	NUM
cana-1051	82	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	82	7	figure	figure	NOUN
cana-1051	82	8	7	7	NUM
cana-1051	82	9	:	:	PUNCT
cana-1051	82	10	residual	residual	ADJ
cana-1051	82	11	acf	acf	NOUN
cana-1051	82	12	and	and	CCONJ
cana-1051	82	13	pacf	pacf	NOUN
cana-1051	82	14	as	as	SCONJ
cana-1051	82	15	the	the	DET
cana-1051	82	16	result	result	NOUN
cana-1051	82	17	indicates	indicate	VERB
cana-1051	82	18	none	none	NOUN
cana-1051	82	19	of	of	ADP
cana-1051	82	20	these	these	DET
cana-1051	82	21	auto	auto	NOUN
cana-1051	82	22	correlations	correlation	NOUN
cana-1051	82	23	of	of	ADP
cana-1051	82	24	residuals	residual	NOUN
cana-1051	82	25	is	be	AUX
cana-1051	82	26	significantly	significantly	ADV
cana-1051	82	27	different	different	ADJ
cana-1051	82	28	from	from	ADP
cana-1051	82	29	0	0	NUM
cana-1051	82	30	at	at	ADP
cana-1051	82	31	the	the	DET
cana-1051	82	32	level	level	NOUN
cana-1051	82	33	0.05	0.05	NUM
cana-1051	82	34	.	.	PUNCT
cana-1051	83	1	the	the	DET
cana-1051	83	2	adequacy	adequacy	NOUN
cana-1051	83	3	of	of	ADP
cana-1051	83	4	the	the	DET
cana-1051	83	5	model	model	NOUN
cana-1051	83	6	is	be	AUX
cana-1051	83	7	tested	test	VERB
cana-1051	83	8	using	use	VERB
cana-1051	83	9	l	l	PROPN
cana-1051	83	10	jung	jung	PROPN
cana-1051	83	11	-	-	PUNCT
cana-1051	83	12	box	box	NOUN
cana-1051	83	13	statistic	statistic	NOUN
cana-1051	83	14	.	.	PUNCT
cana-1051	84	1	l	l	PROPN
cana-1051	84	2	jung	jung	PROPN
cana-1051	84	3	-	-	PUNCT
cana-1051	84	4	box	box	PROPN
cana-1051	84	5	statistic	statistic	NOUN
cana-1051	84	6	is	be	AUX
cana-1051	84	7	21.846	21.846	NUM
cana-1051	84	8	for	for	ADP
cana-1051	84	9	16	16	NUM
cana-1051	84	10	d.	d.	PROPN
cana-1051	84	11	f.	f.	PROPN
cana-1051	84	12	and	and	CCONJ
cana-1051	84	13	the	the	DET
cana-1051	84	14	significant	significant	ADJ
cana-1051	84	15	probability	probability	NOUN
cana-1051	84	16	corresponding	correspond	VERB
cana-1051	84	17	to	to	ADP
cana-1051	84	18	l	l	PROPN
cana-1051	84	19	jung	jung	PROPN
cana-1051	84	20	-	-	PUNCT
cana-1051	84	21	box	box	NOUN
cana-1051	84	22	𝑄	𝑄	PROPN
cana-1051	84	23	statistic	statistic	NOUN
cana-1051	84	24	is	be	AUX
cana-1051	84	25	0.147	0.147	NUM
cana-1051	84	26	which	which	PRON
cana-1051	84	27	is	be	AUX
cana-1051	84	28	greater	great	ADJ
cana-1051	84	29	than	than	ADP
cana-1051	84	30	0.05.therefore	0.05.therefore	ADJ
cana-1051	84	31	null	null	ADJ
cana-1051	84	32	hypothesis	hypothesis	NOUN
cana-1051	84	33	of	of	ADP
cana-1051	84	34	model	model	NOUN
cana-1051	84	35	adequacy	adequacy	NOUN
cana-1051	84	36	and	and	CCONJ
cana-1051	84	37	we	we	PRON
cana-1051	84	38	conclude	conclude	VERB
cana-1051	84	39	that	that	SCONJ
cana-1051	84	40	(	(	PUNCT
cana-1051	84	41	1	1	NUM
cana-1051	84	42	,	,	PUNCT
cana-1051	84	43	1	1	NUM
cana-1051	84	44	,	,	PUNCT
cana-1051	84	45	1	1	X
cana-1051	84	46	)	)	PUNCT
cana-1051	84	47	model	model	NOUN
cana-1051	84	48	is	be	AUX
cana-1051	84	49	an	an	DET
cana-1051	84	50	adequate	adequate	ADJ
cana-1051	84	51	model	model	NOUN
cana-1051	84	52	for	for	ADP
cana-1051	84	53	a	a	DET
cana-1051	84	54	given	give	VERB
cana-1051	84	55	time	time	NOUN
cana-1051	84	56	series	series	NOUN
cana-1051	84	57	.	.	PUNCT
cana-1051	85	1	future	future	ADJ
cana-1051	85	2	exchange	exchange	NOUN
cana-1051	85	3	rates	rate	NOUN
cana-1051	85	4	or	or	CCONJ
cana-1051	85	5	forecasted	forecast	VERB
cana-1051	85	6	using	use	VERB
cana-1051	85	7	minimum	minimum	ADJ
cana-1051	85	8	square	square	ADJ
cana-1051	85	9	error	error	NOUN
cana-1051	85	10	method	method	NOUN
cana-1051	85	11	and	and	CCONJ
cana-1051	85	12	the	the	DET
cana-1051	85	13	forecast	forecast	NOUN
cana-1051	85	14	for	for	ADP
cana-1051	85	15	the	the	DET
cana-1051	85	16	period	period	NOUN
cana-1051	85	17	3rd	3rd	PROPN
cana-1051	85	18	october	october	PROPN
cana-1051	85	19	2023	2023	NUM
cana-1051	85	20	to	to	ADP
cana-1051	85	21	23rd	23rd	ADJ
cana-1051	85	22	october	october	PROPN
cana-1051	85	23	2023	2023	NUM
cana-1051	85	24	are	be	AUX
cana-1051	85	25	following	follow	VERB
cana-1051	85	26	table	table	NOUN
cana-1051	85	27	.	.	PUNCT
cana-1051	86	1	table1	table1	PROPN
cana-1051	86	2	2	2	NUM
cana-1051	86	3	:	:	PUNCT
cana-1051	86	4	forecasts	forecast	NOUN
cana-1051	86	5	of	of	ADP
cana-1051	86	6	exchange	exchange	NOUN
cana-1051	86	7	rates	rate	NOUN
cana-1051	86	8	using	use	VERB
cana-1051	86	9	arima	arima	PROPN
cana-1051	86	10	(	(	PUNCT
cana-1051	86	11	1	1	NUM
cana-1051	86	12	,	,	PUNCT
cana-1051	86	13	1	1	NUM
cana-1051	86	14	,	,	PUNCT
cana-1051	86	15	1	1	NUM
cana-1051	86	16	)	)	PUNCT
cana-1051	86	17	date	date	NOUN
cana-1051	86	18	usd	usd	NOUN
cana-1051	86	19	original	original	ADJ
cana-1051	86	20	values	value	NOUN
cana-1051	86	21	usd	usd	NOUN
cana-1051	86	22	predicted	predict	VERB
cana-1051	86	23	values	value	NOUN
cana-1051	86	24	03.10.23	03.10.23	PROPN
cana-1051	86	25	83.18	83.18	NUM
cana-1051	86	26	83.05	83.05	NUM
cana-1051	87	1	04.10.23	04.10.23	NUM
cana-1051	87	2	83.26	83.26	NUM
cana-1051	87	3	83.19	83.19	NUM
cana-1051	87	4	05.10.23	05.10.23	PROPN
cana-1051	87	5	83.24	83.24	NUM
cana-1051	87	6	83.25	83.25	NUM
cana-1051	87	7	06.10.23	06.10.23	NOUN
cana-1051	87	8	83.24	83.24	NUM
cana-1051	87	9	83.25	83.25	NUM
cana-1051	87	10	09.10.23	09.10.23	NOUN
cana-1051	87	11	83.25	83.25	NUM
cana-1051	87	12	83.23	83.23	NUM
cana-1051	87	13	10.10.23	10.10.23	NUM
cana-1051	87	14	83.26	83.26	NUM
cana-1051	87	15	83.26	83.26	NUM
cana-1051	87	16	11.10.23	11.10.23	PROPN
cana-1051	87	17	83.24	83.24	NUM
cana-1051	87	18	83.25	83.25	NUM
cana-1051	87	19	12.10.23	12.10.23	PROPN
cana-1051	87	20	83.18	83.18	NUM
cana-1051	87	21	83.25	83.25	NUM
cana-1051	87	22	13.10.23	13.10.23	PROPN
cana-1051	87	23	83.26	83.26	NUM
cana-1051	87	24	83.18	83.18	NUM
cana-1051	87	25	16.10.23	16.10.23	NUM
cana-1051	87	26	83.26	83.26	NUM
cana-1051	87	27	83.27	83.27	NUM
cana-1051	87	28	17.10.23	17.10.23	NUM
cana-1051	87	29	83.26	83.26	NUM
cana-1051	87	30	83.26	83.26	NUM
cana-1051	87	31	18.10.23	18.10.23	PROPN
cana-1051	87	32	83.26	83.26	NUM
cana-1051	87	33	83.26	83.26	NUM
cana-1051	87	34	19.10.23	19.10.23	PROPN
cana-1051	87	35	83.27	83.27	NUM
cana-1051	87	36	83.25	83.25	NUM
cana-1051	87	37	20.10.23	20.10.23	PROPN
cana-1051	87	38	83.2	83.2	NUM
cana-1051	87	39	83.28	83.28	NUM
cana-1051	87	40	23.10.23	23.10.23	PROPN
cana-1051	87	41	83.17	83.17	NUM
cana-1051	87	42	83.19	83.19	NUM
cana-1051	87	43	the	the	DET
cana-1051	87	44	graphical	graphical	ADJ
cana-1051	87	45	representation	representation	NOUN
cana-1051	87	46	of	of	ADP
cana-1051	87	47	out	out	ADP
cana-1051	87	48	of	of	ADP
cana-1051	87	49	sample	sample	NOUN
cana-1051	87	50	forecasts	forecast	NOUN
cana-1051	87	51	are	be	AUX
cana-1051	87	52	given	give	VERB
cana-1051	87	53	in	in	ADP
cana-1051	87	54	the	the	DET
cana-1051	87	55	following	follow	VERB
cana-1051	87	56	table	table	NOUN
cana-1051	87	57	communications	communication	NOUN
cana-1051	87	58	on	on	ADP
cana-1051	87	59	applied	apply	VERB
cana-1051	87	60	nonlinear	nonlinear	ADJ
cana-1051	87	61	analysis	analysis	NOUN
cana-1051	87	62	issn	issn	NOUN
cana-1051	87	63	:	:	PUNCT
cana-1051	87	64	1074	1074	NUM
cana-1051	87	65	-	-	PUNCT
cana-1051	87	66	133x	133x	NUM
cana-1051	87	67	vol	vol	NOUN
cana-1051	87	68	31	31	NUM
cana-1051	87	69	no	no	NOUN
cana-1051	87	70	.	.	PUNCT
cana-1051	88	1	5s	5s	NUM
cana-1051	88	2	(	(	PUNCT
cana-1051	88	3	2024	2024	NUM
cana-1051	88	4	)	)	PUNCT
cana-1051	88	5	307	307	NUM
cana-1051	88	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	88	7	figure	figure	NOUN
cana-1051	88	8	8	8	NUM
cana-1051	88	9	:	:	PUNCT
cana-1051	88	10	out	out	ADP
cana-1051	88	11	of	of	ADP
cana-1051	88	12	sample	sample	NOUN
cana-1051	88	13	forecasts	forecast	NOUN
cana-1051	88	14	using	use	VERB
cana-1051	88	15	arima	arima	NOUN
cana-1051	88	16	table	table	NOUN
cana-1051	88	17	3	3	NUM
cana-1051	88	18	:	:	PUNCT
cana-1051	88	19	arima	arima	NOUN
cana-1051	88	20	(	(	PUNCT
cana-1051	88	21	1	1	NUM
cana-1051	88	22	,	,	PUNCT
cana-1051	88	23	1	1	NUM
cana-1051	88	24	,	,	PUNCT
cana-1051	88	25	1	1	X
cana-1051	88	26	)	)	PUNCT
cana-1051	88	27	model	model	NOUN
cana-1051	88	28	performance	performance	NOUN
cana-1051	88	29	mae	mae	PROPN
cana-1051	88	30	mape	mape	PROPN
cana-1051	88	31	mse	mse	PROPN
cana-1051	88	32	rmse	rmse	PROPN
cana-1051	88	33	in	in	ADP
cana-1051	88	34	sample	sample	NOUN
cana-1051	88	35	0.105	0.105	NUM
cana-1051	88	36	0.138046	0.138046	NUM
cana-1051	88	37	0.01105	0.01105	NUM
cana-1051	88	38	0.105119	0.105119	NUM
cana-1051	88	39	out	out	ADP
cana-1051	88	40	sample	sample	NOUN
cana-1051	88	41	0.075	0.075	NUM
cana-1051	88	42	0.090167	0.090167	NUM
cana-1051	88	43	0.00865	0.00865	NUM
cana-1051	88	44	0.093005	0.093005	NUM
cana-1051	88	45	from	from	ADP
cana-1051	88	46	the	the	DET
cana-1051	88	47	above	above	ADJ
cana-1051	88	48	table	table	NOUN
cana-1051	88	49	it	it	PRON
cana-1051	88	50	is	be	AUX
cana-1051	88	51	observed	observe	VERB
cana-1051	88	52	that	that	SCONJ
cana-1051	88	53	arima	arima	PROPN
cana-1051	88	54	model	model	NOUN
cana-1051	88	55	as	as	ADP
cana-1051	88	56	lowest	low	ADJ
cana-1051	88	57	errors	error	NOUN
cana-1051	88	58	measures	measure	NOUN
cana-1051	88	59	in	in	ADP
cana-1051	88	60	fitting	fitting	ADJ
cana-1051	88	61	stage	stage	NOUN
cana-1051	88	62	and	and	CCONJ
cana-1051	88	63	out	out	ADP
cana-1051	88	64	of	of	ADP
cana-1051	88	65	sample	sample	NOUN
cana-1051	88	66	mape	mape	NOUN
cana-1051	88	67	is	be	AUX
cana-1051	88	68	less	less	ADJ
cana-1051	88	69	than	than	ADP
cana-1051	88	70	5	5	NUM
cana-1051	88	71	.	.	PUNCT
cana-1051	89	1	therefore	therefore	ADV
cana-1051	89	2	the	the	DET
cana-1051	89	3	model	model	NOUN
cana-1051	89	4	is	be	AUX
cana-1051	89	5	an	an	DET
cana-1051	89	6	appropriate	appropriate	ADJ
cana-1051	89	7	model	model	NOUN
cana-1051	89	8	for	for	ADP
cana-1051	89	9	forecasting	forecast	VERB
cana-1051	89	10	the	the	DET
cana-1051	89	11	exchange	exchange	NOUN
cana-1051	89	12	rates	rate	NOUN
cana-1051	89	13	.	.	PUNCT
cana-1051	90	1	like	like	ADP
cana-1051	90	2	any	any	DET
cana-1051	90	3	other	other	ADJ
cana-1051	90	4	measure	measure	NOUN
cana-1051	90	5	this	this	DET
cana-1051	90	6	arima	arima	NOUN
cana-1051	90	7	technique	technique	NOUN
cana-1051	90	8	also	also	ADV
cana-1051	90	9	does	do	AUX
cana-1051	90	10	n’t	not	PART
cana-1051	90	11	guaranty	guaranty	VERB
cana-1051	90	12	perfect	perfect	ADJ
cana-1051	90	13	forecast	forecast	NOUN
cana-1051	90	14	.	.	PUNCT
cana-1051	91	1	nevertheless	nevertheless	ADV
cana-1051	91	2	,	,	PUNCT
cana-1051	91	3	it	it	PRON
cana-1051	91	4	can	can	AUX
cana-1051	91	5	be	be	AUX
cana-1051	91	6	successfully	successfully	ADV
cana-1051	91	7	used	use	VERB
cana-1051	91	8	for	for	ADP
cana-1051	91	9	forecasting	forecast	VERB
cana-1051	91	10	long	long	ADJ
cana-1051	91	11	time	time	NOUN
cana-1051	91	12	series	series	PROPN
cana-1051	91	13	data	datum	NOUN
cana-1051	91	14	and	and	CCONJ
cana-1051	91	15	it	it	PRON
cana-1051	91	16	should	should	AUX
cana-1051	91	17	be	be	AUX
cana-1051	91	18	updated	update	VERB
cana-1051	91	19	from	from	ADP
cana-1051	91	20	time	time	NOUN
cana-1051	91	21	with	with	ADP
cana-1051	91	22	incorporation	incorporation	NOUN
cana-1051	91	23	of	of	ADP
cana-1051	91	24	current	current	ADJ
cana-1051	91	25	data	datum	NOUN
cana-1051	91	26	.	.	PUNCT
cana-1051	92	1	4	4	X
cana-1051	92	2	.	.	X
cana-1051	92	3	building	building	NOUN
cana-1051	92	4	of	of	ADP
cana-1051	92	5	feed	feed	NOUN
cana-1051	92	6	forward	forward	ADV
cana-1051	92	7	neural	neural	ADJ
cana-1051	92	8	networks	network	NOUN
cana-1051	92	9	(	(	PUNCT
cana-1051	92	10	ffnn	ffnn	NOUN
cana-1051	92	11	)	)	PUNCT
cana-1051	92	12	the	the	DET
cana-1051	92	13	feed	feed	NOUN
cana-1051	92	14	forward	forward	ADV
cana-1051	92	15	neural	neural	ADJ
cana-1051	92	16	networks	network	NOUN
cana-1051	92	17	model	model	NOUN
cana-1051	92	18	for	for	ADP
cana-1051	92	19	forecasting	forecasting	NOUN
cana-1051	92	20	of	of	ADP
cana-1051	92	21	daily	daily	ADJ
cana-1051	92	22	exchange	exchange	NOUN
cana-1051	92	23	rates	rate	NOUN
cana-1051	92	24	is	be	AUX
cana-1051	92	25	developed	develop	VERB
cana-1051	92	26	using	use	VERB
cana-1051	92	27	spss	spss	PROPN
cana-1051	92	28	packages	package	NOUN
cana-1051	92	29	.	.	PUNCT
cana-1051	93	1	the	the	DET
cana-1051	93	2	ffnn	ffnn	NOUN
cana-1051	93	3	model	model	NOUN
cana-1051	93	4	having	have	VERB
cana-1051	93	5	one	one	NUM
cana-1051	93	6	input	input	NOUN
cana-1051	93	7	layer	layer	NOUN
cana-1051	93	8	,	,	PUNCT
cana-1051	93	9	hidden	hide	VERB
cana-1051	93	10	layers	layer	NOUN
cana-1051	93	11	and	and	CCONJ
cana-1051	93	12	an	an	DET
cana-1051	93	13	output	output	NOUN
cana-1051	93	14	layer	layer	NOUN
cana-1051	93	15	.	.	PUNCT
cana-1051	94	1	the	the	DET
cana-1051	94	2	hyperbolic	hyperbolic	ADJ
cana-1051	94	3	tangent	tangent	NOUN
cana-1051	94	4	function	function	NOUN
cana-1051	94	5	is	be	AUX
cana-1051	94	6	takes	take	VERB
cana-1051	94	7	as	as	ADP
cana-1051	94	8	an	an	DET
cana-1051	94	9	activation	activation	NOUN
cana-1051	94	10	function	function	NOUN
cana-1051	94	11	under	under	ADP
cana-1051	94	12	the	the	DET
cana-1051	94	13	back	back	ADJ
cana-1051	94	14	propagation	propagation	NOUN
cana-1051	94	15	algorithm	algorithm	NOUN
cana-1051	94	16	.	.	PUNCT
cana-1051	95	1	the	the	DET
cana-1051	95	2	ffnn	ffnn	NOUN
cana-1051	95	3	model	model	NOUN
cana-1051	95	4	was	be	AUX
cana-1051	95	5	trained	train	VERB
cana-1051	95	6	till	till	SCONJ
cana-1051	95	7	the	the	DET
cana-1051	95	8	testing	testing	NOUN
cana-1051	95	9	sample	sample	NOUN
cana-1051	95	10	error	error	NOUN
cana-1051	95	11	is	be	AUX
cana-1051	95	12	smaller	small	ADJ
cana-1051	95	13	than	than	ADP
cana-1051	95	14	the	the	DET
cana-1051	95	15	training	training	NOUN
cana-1051	95	16	sample	sample	NOUN
cana-1051	95	17	.	.	PUNCT
cana-1051	96	1	table	table	NOUN
cana-1051	96	2	4	4	NUM
cana-1051	96	3	:	:	PUNCT
cana-1051	96	4	descriptive	descriptive	ADJ
cana-1051	96	5	statistics	statistic	NOUN
cana-1051	96	6	n	n	PRON
cana-1051	96	7	minimum	minimum	ADJ
cana-1051	96	8	maximum	maximum	ADJ
cana-1051	96	9	mean	mean	ADJ
cana-1051	97	1	std	std	NOUN
cana-1051	97	2	.	.	PUNCT
cana-1051	97	3	deviation	deviation	NOUN
cana-1051	97	4	statistic	statistic	NOUN
cana-1051	97	5	statistic	statistic	PROPN
cana-1051	97	6	statistic	statistic	NOUN
cana-1051	97	7	statistic	statistic	PROPN
cana-1051	97	8	statistic	statistic	ADJ
cana-1051	97	9	usd	usd	NOUN
cana-1051	97	10	1164	1164	NUM
cana-1051	97	11	68.3665	68.3665	NUM
cana-1051	97	12	83.2746	83.2746	NUM
cana-1051	97	13	75.646088	75.646088	NUM
cana-1051	97	14	4.3568512	4.3568512	NUM
cana-1051	97	15	lag1	lag1	NOUN
cana-1051	97	16	1163	1163	NUM
cana-1051	97	17	68.37	68.37	NUM
cana-1051	97	18	83.27	83.27	NUM
cana-1051	97	19	75.6396	75.6396	NUM
cana-1051	97	20	4.35313	4.35313	NUM
cana-1051	97	21	valid	valid	ADJ
cana-1051	97	22	n	n	CCONJ
cana-1051	97	23	(	(	PUNCT
cana-1051	97	24	list	list	NOUN
cana-1051	97	25	wise	wise	ADJ
cana-1051	97	26	)	)	PUNCT
cana-1051	97	27	1163	1163	NUM
cana-1051	97	28	by	by	ADP
cana-1051	97	29	applying	apply	VERB
cana-1051	97	30	trial	trial	NOUN
cana-1051	97	31	and	and	CCONJ
cana-1051	97	32	error	error	NOUN
cana-1051	97	33	method	method	NOUN
cana-1051	97	34	,	,	PUNCT
cana-1051	97	35	the	the	DET
cana-1051	97	36	optimum	optimum	ADJ
cana-1051	97	37	number	number	NOUN
cana-1051	97	38	of	of	ADP
cana-1051	97	39	hidden	hide	VERB
cana-1051	97	40	neurons	neuron	NOUN
cana-1051	97	41	is	be	AUX
cana-1051	97	42	four	four	NUM
cana-1051	97	43	and	and	CCONJ
cana-1051	97	44	in	in	ADP
cana-1051	97	45	the	the	DET
cana-1051	97	46	hidden	hide	VERB
cana-1051	97	47	layer	layer	NOUN
cana-1051	97	48	and	and	CCONJ
cana-1051	97	49	the	the	DET
cana-1051	97	50	optimum	optimum	ADJ
cana-1051	97	51	network	network	NOUN
cana-1051	97	52	is	be	AUX
cana-1051	97	53	1	1	NUM
cana-1051	97	54	-	-	SYM
cana-1051	97	55	4	4	NUM
cana-1051	97	56	-	-	SYM
cana-1051	97	57	1	1	NUM
cana-1051	97	58	since	since	SCONJ
cana-1051	97	59	this	this	DET
cana-1051	97	60	network	network	NOUN
cana-1051	97	61	has	have	VERB
cana-1051	97	62	minimum	minimum	PROPN
cana-1051	97	63	mae	mae	PROPN
cana-1051	97	64	,	,	PUNCT
cana-1051	97	65	mape	mape	NOUN
cana-1051	97	66	and	and	CCONJ
cana-1051	97	67	rmse	rmse	NOUN
cana-1051	97	68	.	.	PUNCT
cana-1051	98	1	the	the	DET
cana-1051	98	2	following	follow	VERB
cana-1051	98	3	figure	figure	NOUN
cana-1051	98	4	of	of	ADP
cana-1051	98	5	feed	feed	NOUN
cana-1051	98	6	ward	ward	NOUN
cana-1051	98	7	neural	neural	ADJ
cana-1051	98	8	network	network	NOUN
cana-1051	98	9	gives	give	VERB
cana-1051	98	10	clear	clear	ADJ
cana-1051	98	11	idea	idea	NOUN
cana-1051	98	12	about	about	ADP
cana-1051	98	13	selected	select	VERB
cana-1051	98	14	model	model	NOUN
cana-1051	98	15	for	for	ADP
cana-1051	98	16	the	the	DET
cana-1051	98	17	given	give	VERB
cana-1051	98	18	data	datum	NOUN
cana-1051	98	19	.	.	PUNCT
cana-1051	99	1	communications	communication	NOUN
cana-1051	99	2	on	on	ADP
cana-1051	99	3	applied	apply	VERB
cana-1051	99	4	nonlinear	nonlinear	ADJ
cana-1051	99	5	analysis	analysis	NOUN
cana-1051	99	6	issn	issn	NOUN
cana-1051	99	7	:	:	PUNCT
cana-1051	99	8	1074	1074	NUM
cana-1051	99	9	-	-	PUNCT
cana-1051	99	10	133x	133x	NUM
cana-1051	99	11	vol	vol	NOUN
cana-1051	99	12	31	31	NUM
cana-1051	99	13	no	no	NOUN
cana-1051	99	14	.	.	PUNCT
cana-1051	100	1	5s	5s	NUM
cana-1051	100	2	(	(	PUNCT
cana-1051	100	3	2024	2024	NUM
cana-1051	100	4	)	)	PUNCT
cana-1051	100	5	308	308	NUM
cana-1051	100	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	100	7	figure	figure	NOUN
cana-1051	100	8	9	9	NUM
cana-1051	100	9	:	:	PUNCT
cana-1051	100	10	ffnn	ffnn	NOUN
cana-1051	100	11	model	model	NOUN
cana-1051	100	12	for	for	ADP
cana-1051	100	13	prediction	prediction	NOUN
cana-1051	100	14	of	of	ADP
cana-1051	100	15	inr	inr	PROPN
cana-1051	100	16	-	-	PUNCT
cana-1051	100	17	usd	usd	NOUN
cana-1051	100	18	exchange	exchange	NOUN
cana-1051	100	19	rates	rate	NOUN
cana-1051	100	20	.	.	PUNCT
cana-1051	101	1	the	the	DET
cana-1051	101	2	ffnn	ffnn	NOUN
cana-1051	101	3	model	model	VERB
cana-1051	101	4	1	1	NUM
cana-1051	101	5	-	-	SYM
cana-1051	101	6	4	4	NUM
cana-1051	101	7	-	-	SYM
cana-1051	101	8	1	1	NUM
cana-1051	101	9	parameters	parameter	NOUN
cana-1051	101	10	are	be	AUX
cana-1051	101	11	obtained	obtain	VERB
cana-1051	101	12	and	and	CCONJ
cana-1051	101	13	given	give	VERB
cana-1051	101	14	in	in	ADP
cana-1051	101	15	the	the	DET
cana-1051	101	16	following	follow	VERB
cana-1051	101	17	table	table	NOUN
cana-1051	101	18	table	table	NOUN
cana-1051	101	19	5	5	NUM
cana-1051	101	20	:	:	PUNCT
cana-1051	101	21	ffnn	ffnn	NOUN
cana-1051	101	22	1	1	NUM
cana-1051	101	23	-	-	SYM
cana-1051	101	24	4	4	NUM
cana-1051	101	25	-	-	SYM
cana-1051	101	26	1	1	NUM
cana-1051	101	27	model	model	NOUN
cana-1051	101	28	parameter	parameter	NOUN
cana-1051	101	29	estimates	estimate	NOUN
cana-1051	101	30	parameter	parameter	PROPN
cana-1051	101	31	estimates	estimate	NOUN
cana-1051	101	32	predictor	predictor	NOUN
cana-1051	101	33	predicted	predict	VERB
cana-1051	101	34	hidden	hidden	ADJ
cana-1051	101	35	layer	layer	NOUN
cana-1051	101	36	1	1	NUM
cana-1051	101	37	output	output	NOUN
cana-1051	101	38	layer	layer	NOUN
cana-1051	101	39	h(1:1	h(1:1	NOUN
cana-1051	101	40	)	)	PUNCT
cana-1051	101	41	h(1:2	h(1:2	NOUN
cana-1051	101	42	)	)	PUNCT
cana-1051	101	43	h(1:3	h(1:3	NOUN
cana-1051	101	44	)	)	PUNCT
cana-1051	101	45	h(1:4	h(1:4	NOUN
cana-1051	101	46	)	)	PUNCT
cana-1051	101	47	usd	usd	NOUN
cana-1051	101	48	input	input	NOUN
cana-1051	101	49	layer	layer	NOUN
cana-1051	101	50	(	(	PUNCT
cana-1051	101	51	bias	bias	NOUN
cana-1051	101	52	)	)	PUNCT
cana-1051	101	53	0.161	0.161	NUM
cana-1051	101	54	-0.204	-0.204	NOUN
cana-1051	101	55	0.184	0.184	NUM
cana-1051	101	56	-0.398	-0.398	PUNCT
cana-1051	101	57	lag1	lag1	NOUN
cana-1051	101	58	-0.317	-0.317	PROPN
cana-1051	101	59	0.262	0.262	NUM
cana-1051	101	60	0.285	0.285	NUM
cana-1051	101	61	-0.317	-0.317	PUNCT
cana-1051	101	62	hidden	hide	VERB
cana-1051	101	63	layer	layer	NOUN
cana-1051	101	64	1	1	NUM
cana-1051	101	65	(	(	PUNCT
cana-1051	101	66	bias	bias	NOUN
cana-1051	101	67	)	)	PUNCT
cana-1051	101	68	0.104	0.104	NUM
cana-1051	101	69	h(1:1	h(1:1	NOUN
cana-1051	101	70	)	)	PUNCT
cana-1051	101	71	-1.161	-1.161	ADJ
cana-1051	101	72	h(1:2	h(1:2	NOUN
cana-1051	101	73	)	)	PUNCT
cana-1051	101	74	1.434	1.434	NUM
cana-1051	101	75	h(1:3	h(1:3	NOUN
cana-1051	101	76	)	)	PUNCT
cana-1051	101	77	0.380	0.380	NUM
cana-1051	101	78	h(1:4	h(1:4	NOUN
cana-1051	101	79	)	)	PUNCT
cana-1051	101	80	-0.791	-0.791	PUNCT
cana-1051	101	81	the	the	DET
cana-1051	101	82	hidden	hide	VERB
cana-1051	101	83	activation	activation	NOUN
cana-1051	101	84	functions	function	NOUN
cana-1051	101	85	are	be	AUX
cana-1051	101	86	as	as	SCONJ
cana-1051	101	87	follows	follow	VERB
cana-1051	101	88	:	:	PUNCT
cana-1051	101	89	h11	h11	NOUN
cana-1051	101	90	=	=	SYM
cana-1051	101	91	tanh[0.161	tanh[0.161	PROPN
cana-1051	101	92	-	-	PUNCT
cana-1051	101	93	0.317(zt-1	0.317(zt-1	VERB
cana-1051	101	94	–	–	PUNCT
cana-1051	101	95	75.6396)/	75.6396)/	NUM
cana-1051	101	96	4.3531	4.3531	NUM
cana-1051	101	97	)	)	PUNCT
cana-1051	101	98	]	]	PUNCT
cana-1051	101	99	h12	h12	NOUN
cana-1051	101	100	=	=	PUNCT
cana-1051	101	101	tanh[(-0.204	tanh[(-0.204	NOUN
cana-1051	101	102	-	-	PUNCT
cana-1051	101	103	0.262(zt-1	0.262(zt-1	NOUN
cana-1051	101	104	–	–	PUNCT
cana-1051	101	105	75.6396)/	75.6396)/	NUM
cana-1051	101	106	4.3531	4.3531	NUM
cana-1051	101	107	)	)	PUNCT
cana-1051	101	108	]	]	PUNCT
cana-1051	101	109	h13	h13	NOUN
cana-1051	101	110	=	=	SYM
cana-1051	101	111	tanh[(0.184	tanh[(0.184	NOUN
cana-1051	101	112	-	-	PUNCT
cana-1051	101	113	0.285(zt-1	0.285(zt-1	NOUN
cana-1051	101	114	–	–	PUNCT
cana-1051	101	115	75.6396)/	75.6396)/	NUM
cana-1051	101	116	4.3531	4.3531	NUM
cana-1051	101	117	)	)	PUNCT
cana-1051	101	118	]	]	PUNCT
cana-1051	101	119	h14	h14	NOUN
cana-1051	101	120	=	=	SYM
cana-1051	101	121	tanh[(-0.398	tanh[(-0.398	X
cana-1051	101	122	-0.317(zt-1	-0.317(zt-1	PROPN
cana-1051	101	123	–	–	PUNCT
cana-1051	101	124	75.6396)/	75.6396)/	NUM
cana-1051	101	125	4.3531	4.3531	NUM
cana-1051	101	126	)	)	PUNCT
cana-1051	101	127	]	]	PUNCT
cana-1051	101	128	where	where	SCONJ
cana-1051	101	129	zt-1	zt-1	PRON
cana-1051	101	130	is	be	AUX
cana-1051	101	131	the	the	DET
cana-1051	101	132	rescaled	rescaled	ADJ
cana-1051	101	133	input	input	NOUN
cana-1051	101	134	variable	variable	NOUN
cana-1051	101	135	and	and	CCONJ
cana-1051	101	136	the	the	DET
cana-1051	101	137	forecasting	forecasting	NOUN
cana-1051	101	138	model	model	NOUN
cana-1051	101	139	is	be	AUX
cana-1051	101	140	�	�	NOUN
cana-1051	101	141	̂	̂	NOUN
cana-1051	101	142	�	�	NOUN
cana-1051	101	143	𝑡	𝑡	NOUN
cana-1051	101	144	=	=	SYM
cana-1051	101	145	𝜇𝑧	𝜇𝑧	PROPN
cana-1051	101	146	+	+	X
cana-1051	101	147	𝜎𝑧	𝜎𝑧	PROPN
cana-1051	101	148	(	(	PUNCT
cana-1051	101	149	0.104	0.104	NUM
cana-1051	101	150	−	−	NUM
cana-1051	101	151	1.161𝐻1:1	1.161𝐻1:1	NUM
cana-1051	101	152	+1.434𝐻1:2	+1.434𝐻1:2	PROPN
cana-1051	101	153	+	+	NOUN
cana-1051	101	154	0.380𝐻1:3	0.380𝐻1:3	NOUN
cana-1051	101	155	-	-	PUNCT
cana-1051	101	156	0.791𝐻1:4	0.791𝐻1:4	NOUN
cana-1051	101	157	)	)	PUNCT
cana-1051	102	1	the	the	DET
cana-1051	102	2	forecasting	forecasting	NOUN
cana-1051	102	3	performance	performance	NOUN
cana-1051	102	4	of	of	ADP
cana-1051	102	5	ffnn	ffnn	NOUN
cana-1051	102	6	model	model	NOUN
cana-1051	102	7	is	be	AUX
cana-1051	102	8	given	give	VERB
cana-1051	102	9	below	below	ADP
cana-1051	102	10	table	table	NOUN
cana-1051	102	11	6	6	NUM
cana-1051	102	12	:	:	PUNCT
cana-1051	102	13	ffnn	ffnn	NOUN
cana-1051	102	14	1	1	NUM
cana-1051	102	15	-	-	SYM
cana-1051	102	16	4	4	NUM
cana-1051	102	17	-	-	SYM
cana-1051	102	18	1	1	NUM
cana-1051	102	19	model	model	NOUN
cana-1051	102	20	performance	performance	NOUN
cana-1051	102	21	mae	mae	PROPN
cana-1051	102	22	mape	mape	PROPN
cana-1051	102	23	mse	mse	PROPN
cana-1051	102	24	rmse	rmse	PROPN
cana-1051	102	25	in	in	ADP
cana-1051	102	26	sample	sample	NOUN
cana-1051	102	27	0.117651	0.117651	NUM
cana-1051	103	1	0.150968	0.150968	NUM
cana-1051	103	2	0.01525	0.01525	NUM
cana-1051	103	3	0.12349	0.12349	NUM
cana-1051	103	4	out	out	ADP
cana-1051	103	5	sample	sample	NOUN
cana-1051	103	6	0.171566	0.171566	NUM
cana-1051	103	7	0.206274	0.206274	NUM
cana-1051	103	8	0.029436	0.029436	NUM
cana-1051	103	9	0.171569	0.171569	NUM
cana-1051	103	10	communications	communication	NOUN
cana-1051	103	11	on	on	ADP
cana-1051	103	12	applied	apply	VERB
cana-1051	103	13	nonlinear	nonlinear	ADJ
cana-1051	103	14	analysis	analysis	NOUN
cana-1051	103	15	issn	issn	NOUN
cana-1051	103	16	:	:	PUNCT
cana-1051	103	17	1074	1074	NUM
cana-1051	103	18	-	-	PUNCT
cana-1051	103	19	133x	133x	NUM
cana-1051	103	20	vol	vol	NOUN
cana-1051	103	21	31	31	NUM
cana-1051	103	22	no	no	NOUN
cana-1051	103	23	.	.	PUNCT
cana-1051	104	1	5s	5s	NUM
cana-1051	104	2	(	(	PUNCT
cana-1051	104	3	2024	2024	NUM
cana-1051	104	4	)	)	PUNCT
cana-1051	104	5	309	309	NUM
cana-1051	104	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	104	7	from	from	ADP
cana-1051	104	8	the	the	DET
cana-1051	104	9	above	above	ADJ
cana-1051	104	10	table	table	NOUN
cana-1051	104	11	it	it	PRON
cana-1051	104	12	is	be	AUX
cana-1051	104	13	observed	observe	VERB
cana-1051	104	14	that	that	SCONJ
cana-1051	104	15	the	the	DET
cana-1051	104	16	ffnn	ffnn	NOUN
cana-1051	104	17	model	model	NOUN
cana-1051	104	18	has	have	AUX
cana-1051	104	19	lowest	low	ADJ
cana-1051	104	20	error	error	NOUN
cana-1051	104	21	measures	measure	NOUN
cana-1051	104	22	in	in	ADP
cana-1051	104	23	fitting	fitting	ADJ
cana-1051	104	24	stage	stage	NOUN
cana-1051	104	25	and	and	CCONJ
cana-1051	104	26	forecasting	forecasting	NOUN
cana-1051	104	27	stage	stage	NOUN
cana-1051	104	28	.	.	PUNCT
cana-1051	105	1	since	since	SCONJ
cana-1051	105	2	mape	mape	NOUN
cana-1051	105	3	is	be	AUX
cana-1051	105	4	less	less	ADJ
cana-1051	105	5	than	than	ADP
cana-1051	105	6	5	5	NUM
cana-1051	105	7	therefore	therefore	ADV
cana-1051	105	8	the	the	DET
cana-1051	105	9	ffnn	ffnn	NOUN
cana-1051	105	10	model	model	NOUN
cana-1051	105	11	is	be	AUX
cana-1051	105	12	an	an	DET
cana-1051	105	13	appropriate	appropriate	ADJ
cana-1051	105	14	model	model	NOUN
cana-1051	105	15	for	for	ADP
cana-1051	105	16	forecasting	forecast	VERB
cana-1051	105	17	the	the	DET
cana-1051	105	18	exchange	exchange	NOUN
cana-1051	105	19	rates	rate	NOUN
cana-1051	105	20	.	.	PUNCT
cana-1051	106	1	table	table	NOUN
cana-1051	106	2	7	7	NUM
cana-1051	106	3	:	:	PUNCT
cana-1051	106	4	out	out	ADP
cana-1051	106	5	sample	sample	NOUN
cana-1051	106	6	forecasts	forecast	NOUN
cana-1051	106	7	using	use	VERB
cana-1051	106	8	ffnn	ffnn	NOUN
cana-1051	106	9	1	1	NUM
cana-1051	106	10	-	-	SYM
cana-1051	106	11	4	4	NUM
cana-1051	106	12	-	-	SYM
cana-1051	106	13	1	1	NUM
cana-1051	106	14	date	date	NOUN
cana-1051	106	15	usd	usd	NOUN
cana-1051	106	16	predicted	predict	VERB
cana-1051	106	17	values	value	NOUN
cana-1051	106	18	03	03	NUM
cana-1051	106	19	-	-	PUNCT
cana-1051	106	20	oct-23	oct-23	NOUN
cana-1051	106	21	83.1812	83.1812	NUM
cana-1051	106	22	83.00856	83.00856	NUM
cana-1051	106	23	04	04	NUM
cana-1051	106	24	-	-	PUNCT
cana-1051	106	25	oct-23	oct-23	NOUN
cana-1051	106	26	83.2588	83.2588	NUM
cana-1051	106	27	83.07481	83.07481	NUM
cana-1051	106	28	05	05	NUM
cana-1051	106	29	-	-	PUNCT
cana-1051	106	30	oct-23	oct-23	NOUN
cana-1051	106	31	83.2413	83.2413	NUM
cana-1051	106	32	83.05989	83.05989	NUM
cana-1051	106	33	06	06	NUM
cana-1051	106	34	-	-	PUNCT
cana-1051	106	35	oct-23	oct-23	NOUN
cana-1051	106	36	83.2388	83.2388	NUM
cana-1051	106	37	83.05776	83.05776	NUM
cana-1051	106	38	09	09	NUM
cana-1051	106	39	-	-	PUNCT
cana-1051	106	40	oct-23	oct-23	NOUN
cana-1051	106	41	83.2542	83.2542	NUM
cana-1051	106	42	83.07089	83.07089	NUM
cana-1051	106	43	10	10	NUM
cana-1051	106	44	-	-	PUNCT
cana-1051	106	45	oct-23	oct-23	NOUN
cana-1051	106	46	83.2556	83.2556	NUM
cana-1051	106	47	83.07208	83.07208	NUM
cana-1051	106	48	11	11	NUM
cana-1051	106	49	-	-	PUNCT
cana-1051	106	50	oct-23	oct-23	NOUN
cana-1051	106	51	83.2368	83.2368	NUM
cana-1051	106	52	83.05605	83.05605	NUM
cana-1051	106	53	12	12	NUM
cana-1051	106	54	-	-	PUNCT
cana-1051	106	55	oct-23	oct-23	NOUN
cana-1051	106	56	83.1834	83.1834	NUM
cana-1051	106	57	83.01044	83.01044	NUM
cana-1051	106	58	13	13	NUM
cana-1051	106	59	-	-	PUNCT
cana-1051	106	60	oct-23	oct-23	NOUN
cana-1051	106	61	83.2574	83.2574	NUM
cana-1051	106	62	83.07362	83.07362	NUM
cana-1051	106	63	16	16	NUM
cana-1051	106	64	-	-	PUNCT
cana-1051	106	65	oct-23	oct-23	NOUN
cana-1051	106	66	83.2632	83.2632	NUM
cana-1051	106	67	83.07856	83.07856	NUM
cana-1051	106	68	17	17	NUM
cana-1051	106	69	-	-	PUNCT
cana-1051	106	70	oct-23	oct-23	NOUN
cana-1051	106	71	83.2555	83.2555	NUM
cana-1051	106	72	83.072	83.072	NUM
cana-1051	106	73	18	18	NUM
cana-1051	106	74	-	-	PUNCT
cana-1051	106	75	oct-23	oct-23	NOUN
cana-1051	106	76	83.2571	83.2571	NUM
cana-1051	106	77	83.07336	83.07336	NUM
cana-1051	106	78	19	19	NUM
cana-1051	106	79	-	-	PUNCT
cana-1051	106	80	oct-23	oct-23	NOUN
cana-1051	106	81	83.2746	83.2746	NUM
cana-1051	106	82	83.08826	83.08826	NUM
cana-1051	106	83	20	20	NUM
cana-1051	106	84	-	-	PUNCT
cana-1051	106	85	oct-23	oct-23	NOUN
cana-1051	106	86	83.1982	83.1982	NUM
cana-1051	106	87	83.0231	83.0231	NUM
cana-1051	106	88	23	23	NUM
cana-1051	106	89	-	-	PUNCT
cana-1051	106	90	oct-23	oct-23	NOUN
cana-1051	106	91	83.1663	83.1663	NUM
cana-1051	106	92	82.99581	82.99581	NUM
cana-1051	106	93	the	the	DET
cana-1051	106	94	graphical	graphical	ADJ
cana-1051	106	95	representation	representation	NOUN
cana-1051	106	96	of	of	ADP
cana-1051	106	97	out	out	ADP
cana-1051	106	98	sample	sample	NOUN
cana-1051	106	99	forecasts	forecast	NOUN
cana-1051	106	100	is	be	AUX
cana-1051	106	101	given	give	VERB
cana-1051	106	102	below	below	ADV
cana-1051	106	103	.	.	PUNCT
cana-1051	107	1	this	this	PRON
cana-1051	107	2	presents	present	VERB
cana-1051	107	3	the	the	DET
cana-1051	107	4	forecasts	forecast	NOUN
cana-1051	107	5	generated	generate	VERB
cana-1051	107	6	from	from	ADP
cana-1051	107	7	ffnn	ffnn	NOUN
cana-1051	107	8	model	model	NOUN
cana-1051	107	9	is	be	AUX
cana-1051	107	10	close	close	ADJ
cana-1051	107	11	to	to	ADP
cana-1051	107	12	original	original	ADJ
cana-1051	107	13	series	series	NOUN
cana-1051	107	14	and	and	CCONJ
cana-1051	107	15	it	it	PRON
cana-1051	107	16	indicates	indicate	VERB
cana-1051	107	17	a	a	DET
cana-1051	107	18	good	good	ADJ
cana-1051	107	19	performance	performance	NOUN
cana-1051	107	20	of	of	ADP
cana-1051	107	21	ffnn	ffnn	NOUN
cana-1051	107	22	model	model	NOUN
cana-1051	107	23	.	.	PUNCT
cana-1051	108	1	figure	figure	VERB
cana-1051	108	2	10	10	NUM
cana-1051	108	3	:	:	PUNCT
cana-1051	108	4	forecasts	forecast	NOUN
cana-1051	108	5	of	of	ADP
cana-1051	108	6	usd	usd	PROPN
cana-1051	108	7	-	-	PUNCT
cana-1051	108	8	inr	inr	NOUN
cana-1051	108	9	exchange	exchange	NOUN
cana-1051	108	10	rates	rate	NOUN
cana-1051	108	11	using	use	VERB
cana-1051	108	12	ffnn	ffnn	NOUN
cana-1051	108	13	model	model	NOUN
cana-1051	108	14	5	5	NUM
cana-1051	108	15	.	.	PUNCT
cana-1051	108	16	comparision	comparision	NOUN
cana-1051	108	17	and	and	CCONJ
cana-1051	108	18	conclusions	conclusion	NOUN
cana-1051	108	19	the	the	DET
cana-1051	108	20	performance	performance	NOUN
cana-1051	108	21	of	of	ADP
cana-1051	108	22	arima	arima	PROPN
cana-1051	108	23	,	,	PUNCT
cana-1051	108	24	ffnn	ffnn	NOUN
cana-1051	108	25	models	model	NOUN
cana-1051	108	26	and	and	CCONJ
cana-1051	108	27	its	its	PRON
cana-1051	108	28	graphical	graphical	ADJ
cana-1051	108	29	representation	representation	NOUN
cana-1051	108	30	is	be	AUX
cana-1051	108	31	given	give	VERB
cana-1051	108	32	below	below	ADP
cana-1051	108	33	:	:	PUNCT
cana-1051	108	34	table	table	NOUN
cana-1051	108	35	8	8	NUM
cana-1051	108	36	:	:	PUNCT
cana-1051	108	37	performance	performance	NOUN
cana-1051	108	38	of	of	ADP
cana-1051	108	39	the	the	DET
cana-1051	108	40	arima	arima	PROPN
cana-1051	108	41	and	and	CCONJ
cana-1051	108	42	ffnn	ffnn	NOUN
cana-1051	108	43	models	model	NOUN
cana-1051	108	44	model	model	VERB
cana-1051	108	45	in	in	ADP
cana-1051	108	46	sample	sample	NOUN
cana-1051	108	47	out	out	ADP
cana-1051	108	48	sample	sample	NOUN
cana-1051	108	49	mae	mae	PROPN
cana-1051	108	50	mape	mape	PROPN
cana-1051	108	51	rmse	rmse	PROPN
cana-1051	108	52	mae	mae	PROPN
cana-1051	108	53	mape	mape	PROPN
cana-1051	108	54	rmse	rmse	PROPN
cana-1051	108	55	arima	arima	PROPN
cana-1051	108	56	0.105	0.105	NUM
cana-1051	108	57	0.138046	0.138046	NUM
cana-1051	108	58	0.105119	0.105119	NUM
cana-1051	108	59	0.075	0.075	NUM
cana-1051	108	60	0.090167	0.090167	NUM
cana-1051	108	61	0.093005	0.093005	NUM
cana-1051	108	62	ffnn	ffnn	NOUN
cana-1051	108	63	0.117651	0.117651	NUM
cana-1051	108	64	0.150968	0.150968	NUM
cana-1051	108	65	0.12349	0.12349	NUM
cana-1051	108	66	0.171566	0.171566	NUM
cana-1051	108	67	0.206274	0.206274	NUM
cana-1051	108	68	0.171569	0.171569	NUM
cana-1051	108	69	communications	communication	NOUN
cana-1051	108	70	on	on	ADP
cana-1051	108	71	applied	apply	VERB
cana-1051	108	72	nonlinear	nonlinear	ADJ
cana-1051	108	73	analysis	analysis	NOUN
cana-1051	108	74	issn	issn	NOUN
cana-1051	108	75	:	:	PUNCT
cana-1051	108	76	1074	1074	NUM
cana-1051	108	77	-	-	PUNCT
cana-1051	108	78	133x	133x	NUM
cana-1051	108	79	vol	vol	NOUN
cana-1051	108	80	31	31	NUM
cana-1051	108	81	no	no	NOUN
cana-1051	108	82	.	.	PUNCT
cana-1051	109	1	5s	5s	NUM
cana-1051	109	2	(	(	PUNCT
cana-1051	109	3	2024	2024	NUM
cana-1051	109	4	)	)	PUNCT
cana-1051	109	5	310	310	NUM
cana-1051	109	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	109	7	figure	figure	NOUN
cana-1051	109	8	11	11	NUM
cana-1051	109	9	:	:	PUNCT
cana-1051	109	10	out	out	ADP
cana-1051	109	11	of	of	ADP
cana-1051	109	12	sample	sample	NOUN
cana-1051	109	13	forecasts	forecast	NOUN
cana-1051	109	14	of	of	ADP
cana-1051	109	15	arima	arima	PROPN
cana-1051	109	16	and	and	CCONJ
cana-1051	109	17	ffnn	ffnn	NOUN
cana-1051	109	18	models	model	NOUN
cana-1051	109	19	from	from	ADP
cana-1051	109	20	the	the	DET
cana-1051	109	21	above	above	ADJ
cana-1051	109	22	table	table	NOUN
cana-1051	109	23	,	,	PUNCT
cana-1051	109	24	it	it	PRON
cana-1051	109	25	is	be	AUX
cana-1051	109	26	observed	observe	VERB
cana-1051	109	27	that	that	SCONJ
cana-1051	109	28	arima	arima	NOUN
cana-1051	109	29	and	and	CCONJ
cana-1051	109	30	ffnn	ffnn	NOUN
cana-1051	109	31	models	model	NOUN
cana-1051	109	32	has	have	VERB
cana-1051	109	33	moderately	moderately	ADV
cana-1051	109	34	minimum	minimum	ADJ
cana-1051	109	35	error	error	NOUN
cana-1051	109	36	measures	measure	NOUN
cana-1051	109	37	in	in	ADP
cana-1051	109	38	-	-	PUNCT
cana-1051	109	39	sample	sample	NOUN
cana-1051	109	40	and	and	CCONJ
cana-1051	109	41	out	out	ADP
cana-1051	109	42	-	-	PUNCT
cana-1051	109	43	of	of	ADP
cana-1051	109	44	-	-	PUNCT
cana-1051	109	45	sample	sample	NOUN
cana-1051	109	46	.	.	PUNCT
cana-1051	110	1	one	one	PRON
cana-1051	110	2	can	can	AUX
cana-1051	110	3	use	use	VERB
cana-1051	110	4	either	either	CCONJ
cana-1051	110	5	arima	arima	NOUN
cana-1051	110	6	or	or	CCONJ
cana-1051	110	7	ffnn	ffnn	NOUN
cana-1051	110	8	model	model	NOUN
cana-1051	110	9	to	to	ADP
cana-1051	110	10	forecasts	forecast	NOUN
cana-1051	110	11	of	of	ADP
cana-1051	110	12	daily	daily	ADJ
cana-1051	110	13	exchange	exchange	NOUN
cana-1051	110	14	rates	rate	NOUN
cana-1051	110	15	for	for	ADP
cana-1051	110	16	usd	usd	NOUN
cana-1051	110	17	.	.	PUNCT
cana-1051	111	1	but	but	CCONJ
cana-1051	111	2	by	by	ADP
cana-1051	111	3	adopting	adopt	VERB
cana-1051	111	4	ffnn	ffnn	NOUN
cana-1051	111	5	model	model	NOUN
cana-1051	111	6	can	can	AUX
cana-1051	111	7	study	study	VERB
cana-1051	111	8	the	the	DET
cana-1051	111	9	complexity	complexity	NOUN
cana-1051	111	10	and	and	CCONJ
cana-1051	111	11	non	non	ADJ
cana-1051	111	12	linearity	linearity	NOUN
cana-1051	111	13	of	of	ADP
cana-1051	111	14	the	the	DET
cana-1051	111	15	data	datum	NOUN
cana-1051	111	16	.	.	PUNCT
cana-1051	112	1	hence	hence	ADV
cana-1051	112	2	it	it	PRON
cana-1051	112	3	was	be	AUX
cana-1051	112	4	concluded	conclude	VERB
cana-1051	112	5	that	that	SCONJ
cana-1051	112	6	ffnn	ffnn	NOUN
cana-1051	112	7	and	and	CCONJ
cana-1051	112	8	arima	arima	NOUN
cana-1051	112	9	models	model	NOUN
cana-1051	112	10	are	be	AUX
cana-1051	112	11	equally	equally	ADV
cana-1051	112	12	performed	perform	VERB
cana-1051	112	13	for	for	ADP
cana-1051	112	14	the	the	DET
cana-1051	112	15	daily	daily	ADJ
cana-1051	112	16	exchange	exchange	NOUN
cana-1051	112	17	rates	rate	NOUN
cana-1051	112	18	of	of	ADP
cana-1051	112	19	usd	usd	NOUN
cana-1051	112	20	table	table	NOUN
cana-1051	112	21	9	9	NUM
cana-1051	112	22	:	:	PUNCT
cana-1051	112	23	friedman	friedman	PROPN
cana-1051	112	24	’s	’s	PART
cana-1051	112	25	test	test	NOUN
cana-1051	112	26	for	for	ADP
cana-1051	112	27	out	out	ADP
cana-1051	112	28	of	of	ADP
cana-1051	112	29	sample	sample	NOUN
cana-1051	112	30	forecasts	forecast	NOUN
cana-1051	112	31	sample	sample	NOUN
cana-1051	112	32	model	model	NOUN
cana-1051	112	33	mean	mean	VERB
cana-1051	112	34	ranks	rank	VERB
cana-1051	112	35	n	n	CCONJ
cana-1051	112	36	chi	chi	ADJ
cana-1051	112	37	-	-	PUNCT
cana-1051	112	38	square	square	ADJ
cana-1051	112	39	p	p	NOUN
cana-1051	112	40	-	-	PUNCT
cana-1051	112	41	value	value	NOUN
cana-1051	112	42	out	out	ADV
cana-1051	112	43	-	-	PUNCT
cana-1051	112	44	of	of	ADP
cana-1051	112	45	-	-	PUNCT
cana-1051	112	46	sample	sample	NOUN
cana-1051	112	47	arima	arima	NOUN
cana-1051	112	48	1.03	1.03	NUM
cana-1051	112	49	15	15	NUM
cana-1051	112	50	14.00	14.00	NUM
cana-1051	112	51	<	<	SYM
cana-1051	112	52	0.001	0.001	NUM
cana-1051	112	53	ffnn	ffnn	NOUN
cana-1051	112	54	1.97	1.97	NUM
cana-1051	112	55	it	it	PRON
cana-1051	112	56	is	be	AUX
cana-1051	112	57	noticed	notice	VERB
cana-1051	112	58	that	that	SCONJ
cana-1051	112	59	the	the	DET
cana-1051	112	60	significant	significant	ADJ
cana-1051	112	61	possibility	possibility	NOUN
cana-1051	112	62	is	be	AUX
cana-1051	112	63	less	less	ADJ
cana-1051	112	64	than	than	ADP
cana-1051	112	65	0.05	0.05	NUM
cana-1051	112	66	,	,	PUNCT
cana-1051	112	67	therefore	therefore	ADV
cana-1051	112	68	the	the	DET
cana-1051	112	69	invalid	invalid	ADJ
cana-1051	112	70	speculation	speculation	NOUN
cana-1051	112	71	is	be	AUX
cana-1051	112	72	dismissed	dismiss	VERB
cana-1051	112	73	and	and	CCONJ
cana-1051	112	74	presume	presume	VERB
cana-1051	112	75	that	that	SCONJ
cana-1051	112	76	there	there	PRON
cana-1051	112	77	is	be	VERB
cana-1051	112	78	a	a	DET
cana-1051	112	79	critical	critical	ADJ
cana-1051	112	80	difference	difference	NOUN
cana-1051	112	81	in	in	ADP
cana-1051	112	82	the	the	DET
cana-1051	112	83	performance	performance	NOUN
cana-1051	112	84	of	of	ADP
cana-1051	112	85	the	the	DET
cana-1051	112	86	models	model	NOUN
cana-1051	112	87	.	.	PUNCT
cana-1051	113	1	but	but	CCONJ
cana-1051	113	2	based	base	VERB
cana-1051	113	3	on	on	ADP
cana-1051	113	4	the	the	DET
cana-1051	113	5	mean	mean	ADJ
cana-1051	113	6	ranks	rank	NOUN
cana-1051	113	7	of	of	ADP
cana-1051	113	8	the	the	DET
cana-1051	113	9	models	model	NOUN
cana-1051	113	10	arima	arima	PROPN
cana-1051	113	11	gets	get	VERB
cana-1051	113	12	first	first	ADJ
cana-1051	113	13	rank	rank	NOUN
cana-1051	113	14	and	and	CCONJ
cana-1051	113	15	ffnn	ffnn	NOUN
cana-1051	113	16	gets	get	VERB
cana-1051	113	17	the	the	DET
cana-1051	113	18	second	second	ADJ
cana-1051	113	19	rank	rank	NOUN
cana-1051	113	20	.	.	PUNCT
cana-1051	114	1	therefore	therefore	ADV
cana-1051	114	2	arima	arima	PROPN
cana-1051	114	3	model	model	NOUN
cana-1051	114	4	moderately	moderately	ADV
cana-1051	114	5	performs	perform	VERB
cana-1051	114	6	better	well	ADJ
cana-1051	114	7	than	than	ADP
cana-1051	114	8	ffnn	ffnn	NOUN
cana-1051	114	9	model	model	NOUN
cana-1051	114	10	.	.	PUNCT
cana-1051	115	1	references	reference	NOUN
cana-1051	115	2	[	[	X
cana-1051	115	3	1	1	NUM
cana-1051	115	4	]	]	X
cana-1051	115	5	haykin	haykin	PROPN
cana-1051	115	6	,	,	PUNCT
cana-1051	115	7	s.s	s.s	PROPN
cana-1051	115	8	.	.	PROPN
cana-1051	115	9	,	,	PUNCT
cana-1051	115	10	1999	1999	NUM
cana-1051	115	11	,	,	PUNCT
cana-1051	115	12	“	"	PUNCT
cana-1051	115	13	neural	neural	ADJ
cana-1051	115	14	networks	network	NOUN
cana-1051	115	15	.	.	PUNCT
cana-1051	116	1	a	a	DET
cana-1051	116	2	comprehensive	comprehensive	ADJ
cana-1051	116	3	foundation	foundation	NOUN
cana-1051	116	4	”	"	PUNCT
cana-1051	116	5	.	.	PUNCT
cana-1051	117	1	upper	upper	ADJ
cana-1051	117	2	saddle	saddle	PROPN
cana-1051	117	3	river	river	PROPN
cana-1051	117	4	,	,	PUNCT
cana-1051	117	5	n.j	n.j	PROPN
cana-1051	117	6	.	.	PROPN
cana-1051	117	7	,	,	PUNCT
cana-1051	117	8	printice	printice	NOUN
cana-1051	117	9	hall	hall	NOUN
cana-1051	117	10	.	.	PUNCT
cana-1051	118	1	[	[	X
cana-1051	118	2	2	2	NUM
cana-1051	118	3	]	]	X
cana-1051	118	4	huang	huang	PROPN
cana-1051	118	5	,	,	PUNCT
cana-1051	118	6	w.	w.	PROPN
cana-1051	118	7	,	,	PUNCT
cana-1051	118	8	lai	lai	PROPN
cana-1051	118	9	,	,	PUNCT
cana-1051	118	10	k.	k.	PROPN
cana-1051	118	11	k.	k.	PROPN
cana-1051	118	12	,	,	PUNCT
cana-1051	118	13	nakamoni	nakamoni	PROPN
cana-1051	118	14	,	,	PUNCT
cana-1051	118	15	y.	y.	PROPN
cana-1051	118	16	and	and	CCONJ
cana-1051	118	17	wang	wang	PROPN
cana-1051	118	18	,	,	PUNCT
cana-1051	118	19	s.	s.	PROPN
cana-1051	118	20	,	,	PUNCT
cana-1051	118	21	(	(	PUNCT
cana-1051	118	22	2004	2004	NUM
cana-1051	118	23	)	)	PUNCT
cana-1051	118	24	,	,	PUNCT
cana-1051	118	25	forecasting	forecast	VERB
cana-1051	118	26	foreign	foreign	ADJ
cana-1051	118	27	exchange	exchange	NOUN
cana-1051	118	28	rates	rate	NOUN
cana-1051	118	29	with	with	ADP
cana-1051	118	30	artificial	artificial	ADJ
cana-1051	118	31	neural	neural	ADJ
cana-1051	118	32	networks	network	NOUN
cana-1051	118	33	:	:	PUNCT
cana-1051	118	34	a	a	DET
cana-1051	118	35	review	review	NOUN
cana-1051	118	36	,	,	PUNCT
cana-1051	118	37	international	international	ADJ
cana-1051	118	38	journal	journal	NOUN
cana-1051	118	39	of	of	ADP
cana-1051	118	40	information	information	NOUN
cana-1051	118	41	technology	technology	NOUN
cana-1051	118	42	and	and	CCONJ
cana-1051	118	43	decision	decision	NOUN
cana-1051	118	44	making	making	NOUN
cana-1051	118	45	,	,	PUNCT
cana-1051	118	46	3(1	3(1	NUM
cana-1051	118	47	)	)	PUNCT
cana-1051	118	48	,	,	PUNCT
cana-1051	118	49	145	145	NUM
cana-1051	118	50	-	-	SYM
cana-1051	118	51	165	165	NUM
cana-1051	118	52	.	.	PUNCT
cana-1051	119	1	[	[	X
cana-1051	119	2	3	3	NUM
cana-1051	119	3	]	]	X
cana-1051	119	4	kamruzzaman	kamruzzaman	NOUN
cana-1051	119	5	,	,	PUNCT
cana-1051	119	6	j.	j.	PROPN
cana-1051	119	7	,	,	PUNCT
cana-1051	119	8	and	and	CCONJ
cana-1051	119	9	sarkar	sarkar	PROPN
cana-1051	119	10	r.a	r.a	PROPN
cana-1051	119	11	.	.	PROPN
cana-1051	119	12	(	(	PUNCT
cana-1051	119	13	2004	2004	NUM
cana-1051	119	14	)	)	PUNCT
cana-1051	119	15	,	,	PUNCT
cana-1051	119	16	“	"	PUNCT
cana-1051	119	17	ann	ann	PROPN
cana-1051	119	18	-	-	PUNCT
cana-1051	119	19	based	base	VERB
cana-1051	119	20	forecasting	forecasting	NOUN
cana-1051	119	21	of	of	ADP
cana-1051	119	22	foreign	foreign	ADJ
cana-1051	119	23	currency	currency	NOUN
cana-1051	119	24	exchange	exchange	NOUN
cana-1051	119	25	rates	rate	NOUN
cana-1051	119	26	”	"	PUNCT
cana-1051	119	27	neural	neural	ADJ
cana-1051	119	28	information	information	NOUN
cana-1051	119	29	processing	processing	NOUN
cana-1051	119	30	–	–	PUNCT
cana-1051	119	31	letters	letter	NOUN
cana-1051	119	32	and	and	CCONJ
cana-1051	119	33	reviews	review	NOUN
cana-1051	119	34	3(2	3(2	NUM
cana-1051	119	35	)	)	PUNCT
cana-1051	119	36	,	,	PUNCT
cana-1051	119	37	49	49	NUM
cana-1051	119	38	-	-	SYM
cana-1051	119	39	58	58	NUM
cana-1051	119	40	.	.	PUNCT
cana-1051	120	1	[	[	X
cana-1051	120	2	4	4	NUM
cana-1051	120	3	]	]	PUNCT
cana-1051	120	4	krishna	krishna	PROPN
cana-1051	120	5	reddy	reddy	PROPN
cana-1051	120	6	,	,	PUNCT
cana-1051	120	7	m	m	PROPN
cana-1051	120	8	and	and	CCONJ
cana-1051	120	9	kalyani	kalyani	PROPN
cana-1051	120	10	,	,	PUNCT
cana-1051	120	11	d	d	X
cana-1051	120	12	(	(	PUNCT
cana-1051	120	13	2005	2005	NUM
cana-1051	120	14	)	)	PUNCT
cana-1051	120	15	,	,	PUNCT
cana-1051	120	16	“	"	PUNCT
cana-1051	120	17	applications	application	NOUN
cana-1051	120	18	of	of	ADP
cana-1051	120	19	neural	neural	ADJ
cana-1051	120	20	networks	network	NOUN
cana-1051	120	21	in	in	ADP
cana-1051	120	22	time	time	NOUN
cana-1051	120	23	series	series	PROPN
cana-1051	120	24	forecasting	forecasting	PROPN
cana-1051	120	25	”	"	PUNCT
cana-1051	120	26	,	,	PUNCT
cana-1051	120	27	international	international	ADJ
cana-1051	120	28	journal	journal	NOUN
cana-1051	120	29	of	of	ADP
cana-1051	120	30	management	management	NOUN
cana-1051	120	31	and	and	CCONJ
cana-1051	120	32	systems	system	NOUN
cana-1051	120	33	,	,	PUNCT
cana-1051	120	34	vol	vol	NOUN
cana-1051	120	35	.	.	PROPN
cana-1051	120	36	21	21	NUM
cana-1051	120	37	.	.	X
cana-1051	120	38	53	53	NUM
cana-1051	120	39	-	-	SYM
cana-1051	120	40	64	64	NUM
cana-1051	120	41	.	.	PUNCT
cana-1051	121	1	[	[	X
cana-1051	121	2	5	5	NUM
cana-1051	121	3	]	]	X
cana-1051	121	4	kuan	kuan	PROPN
cana-1051	121	5	,	,	PUNCT
cana-1051	121	6	c.	c.	PROPN
cana-1051	121	7	m.	m.	NOUN
cana-1051	121	8	,	,	PUNCT
cana-1051	121	9	and	and	CCONJ
cana-1051	121	10	t.liu	t.liu	PROPN
cana-1051	121	11	(	(	PUNCT
cana-1051	121	12	1995	1995	NUM
cana-1051	121	13	)	)	PUNCT
cana-1051	121	14	,	,	PUNCT
cana-1051	121	15	“	"	PUNCT
cana-1051	121	16	forecasting	forecast	VERB
cana-1051	121	17	foreign	foreign	ADJ
cana-1051	121	18	exchange	exchange	NOUN
cana-1051	121	19	rates	rate	NOUN
cana-1051	121	20	using	use	VERB
cana-1051	121	21	feedforward	feedforward	NOUN
cana-1051	121	22	and	and	CCONJ
cana-1051	121	23	recurrent	recurrent	ADJ
cana-1051	121	24	neural	neural	ADJ
cana-1051	121	25	networks	network	NOUN
cana-1051	121	26	”	"	PUNCT
cana-1051	121	27	.	.	PUNCT
cana-1051	122	1	journal	journal	PROPN
cana-1051	122	2	of	of	ADP
cana-1051	122	3	applied	applied	ADJ
cana-1051	122	4	econometrics	econometric	NOUN
cana-1051	122	5	,	,	PUNCT
cana-1051	122	6	10:347	10:347	NUM
cana-1051	122	7	-	-	SYM
cana-1051	122	8	364	364	NUM
cana-1051	122	9	.	.	PUNCT
cana-1051	123	1	[	[	X
cana-1051	123	2	6	6	NUM
cana-1051	123	3	]	]	PUNCT
cana-1051	123	4	k.murali	k.murali	ADJ
cana-1051	123	5	krishna	krishna	PROPN
cana-1051	123	6	,	,	PUNCT
cana-1051	123	7	dr.m	dr.m	PROPN
cana-1051	123	8	.	.	PUNCT
cana-1051	123	9	raghavender	raghavender	NOUN
cana-1051	123	10	sharma	sharma	PROPN
cana-1051	123	11	and	and	CCONJ
cana-1051	123	12	dr.n.konda	dr.n.konda	PROPN
cana-1051	123	13	reddy	reddy	PROPN
cana-1051	123	14	,	,	PUNCT
cana-1051	123	15	forecasting	forecasting	NOUN
cana-1051	123	16	of	of	ADP
cana-1051	123	17	silver	silver	NOUN
cana-1051	123	18	prices	price	NOUN
cana-1051	123	19	using	use	VERB
cana-1051	123	20	artificial	artificial	ADJ
cana-1051	123	21	neural	neural	ADJ
cana-1051	123	22	networks	network	NOUN
cana-1051	123	23	.	.	PUNCT
cana-1051	124	1	jardcs	jardcs	PROPN
cana-1051	124	2	,	,	PUNCT
cana-1051	124	3	volume	volume	NOUN
cana-1051	124	4	10	10	NUM
cana-1051	124	5	,	,	PUNCT
cana-1051	124	6	06	06	NUM
cana-1051	124	7	issue	issue	NOUN
cana-1051	124	8	2018	2018	NUM
cana-1051	124	9	.	.	PUNCT
cana-1051	125	1	[	[	X
cana-1051	125	2	7	7	NUM
cana-1051	125	3	]	]	X
cana-1051	125	4	k.murali	k.murali	ADJ
cana-1051	125	5	krishna	krishna	PROPN
cana-1051	125	6	,	,	PUNCT
cana-1051	125	7	dr.m.raghavender	dr.m.raghavender	PROPN
cana-1051	125	8	sharma	sharma	NOUN
cana-1051	125	9	and	and	CCONJ
cana-1051	125	10	dr.n	dr.n	PROPN
cana-1051	125	11	.	.	PUNCT
cana-1051	126	1	konda	konda	PROPN
cana-1051	126	2	reddy	reddy	PROPN
cana-1051	126	3	,	,	PUNCT
cana-1051	126	4	forecasting	forecasting	NOUN
cana-1051	126	5	of	of	ADP
cana-1051	126	6	daily	daily	ADJ
cana-1051	126	7	prices	price	NOUN
cana-1051	126	8	of	of	ADP
cana-1051	126	9	gold	gold	NOUN
cana-1051	126	10	in	in	ADP
cana-1051	126	11	india	india	PROPN
cana-1051	126	12	using	use	VERB
cana-1051	126	13	arima	arima	PROPN
cana-1051	126	14	and	and	CCONJ
cana-1051	126	15	ffnn	ffnn	NOUN
cana-1051	126	16	models	model	NOUN
cana-1051	126	17	.	.	PUNCT
cana-1051	127	1	ijeat	ijeat	PROPN
cana-1051	127	2	,	,	PUNCT
cana-1051	127	3	issn:2249	issn:2249	PROPN
cana-1051	127	4	-	-	SYM
cana-1051	127	5	3958,volume-8,issue-3,february	3958,volume-8,issue-3,february	NUM
cana-1051	127	6	2019	2019	NUM
cana-1051	127	7	.	.	PUNCT
cana-1051	128	1	[	[	X
cana-1051	128	2	8	8	NUM
cana-1051	128	3	]	]	SYM
cana-1051	128	4	marrewijk	marrewijk	NOUN
cana-1051	128	5	,	,	PUNCT
cana-1051	128	6	c.	c.	PROPN
cana-1051	128	7	(	(	PUNCT
cana-1051	128	8	2004	2004	NUM
cana-1051	128	9	)	)	PUNCT
cana-1051	128	10	,	,	PUNCT
cana-1051	128	11	an	an	DET
cana-1051	128	12	introduction	introduction	NOUN
cana-1051	128	13	to	to	ADP
cana-1051	128	14	international	international	ADJ
cana-1051	128	15	money	money	NOUN
cana-1051	128	16	and	and	CCONJ
cana-1051	128	17	foreign	foreign	ADJ
cana-1051	128	18	exchange	exchange	NOUN
cana-1051	128	19	markets	market	NOUN
cana-1051	128	20	,	,	PUNCT
cana-1051	128	21	discussion	discussion	NOUN
cana-1051	128	22	paper	paper	NOUN
cana-1051	128	23	no	no	NOUN
cana-1051	128	24	.	.	PROPN
cana-1051	128	25	0407	0407	NUM
cana-1051	128	26	,	,	PUNCT
cana-1051	128	27	centre	centre	NOUN
cana-1051	128	28	for	for	ADP
cana-1051	128	29	international	international	ADJ
cana-1051	128	30	economic	economic	ADJ
cana-1051	128	31	studies	study	NOUN
cana-1051	128	32	,	,	PUNCT
cana-1051	128	33	university	university	PROPN
cana-1051	128	34	of	of	ADP
cana-1051	128	35	adelaide	adelaide	PROPN
cana-1051	128	36	,	,	PUNCT
cana-1051	128	37	australia	australia	PROPN
cana-1051	128	38	.	.	PUNCT
cana-1051	129	1	[	[	X
cana-1051	129	2	9	9	NUM
cana-1051	129	3	]	]	PUNCT
cana-1051	129	4	l.	l.	PROPN
cana-1051	129	5	jung	jung	PROPN
cana-1051	129	6	.	.	PUNCT
cana-1051	129	7	g.m	g.m	PROPN
cana-1051	129	8	and	and	CCONJ
cana-1051	129	9	box	box	PROPN
cana-1051	129	10	,	,	PUNCT
cana-1051	129	11	ge.p	ge.p	PROPN
cana-1051	129	12	,	,	PUNCT
cana-1051	129	13	(	(	PUNCT
cana-1051	129	14	1979	1979	NUM
cana-1051	129	15	)	)	PUNCT
cana-1051	129	16	“	"	PUNCT
cana-1051	129	17	on	on	ADP
cana-1051	129	18	a	a	DET
cana-1051	129	19	measure	measure	NOUN
cana-1051	129	20	of	of	ADP
cana-1051	129	21	lack	lack	NOUN
cana-1051	129	22	fit	fit	ADJ
cana-1051	129	23	in	in	ADP
cana-1051	129	24	time	time	NOUN
cana-1051	129	25	series	series	NOUN
cana-1051	129	26	models	model	NOUN
cana-1051	129	27	”	"	PUNCT
cana-1051	129	28	.	.	PUNCT
cana-1051	130	1	[	[	X
cana-1051	130	2	10	10	NUM
cana-1051	130	3	]	]	X
cana-1051	130	4	pami	pami	NOUN
cana-1051	130	5	dua	dua	PROPN
cana-1051	130	6	and	and	CCONJ
cana-1051	130	7	rajiv	rajiv	PROPN
cana-1051	130	8	ranjan	ranjan	PROPN
cana-1051	130	9	(	(	PUNCT
cana-1051	130	10	2011	2011	NUM
cana-1051	130	11	)	)	PUNCT
cana-1051	130	12	,	,	PUNCT
cana-1051	130	13	modelling	modelling	NOUN
cana-1051	130	14	and	and	CCONJ
cana-1051	130	15	forecasting	forecast	VERB
cana-1051	130	16	the	the	DET
cana-1051	130	17	indian	indian	PROPN
cana-1051	130	18	re	re	PROPN
cana-1051	130	19	/	/	PROPN
cana-1051	130	20	us	us	PROPN
cana-1051	130	21	dollar	dollar	NOUN
cana-1051	130	22	exchange	exchange	NOUN
cana-1051	130	23	rate	rate	NOUN
cana-1051	130	24	,	,	PUNCT
cana-1051	130	25	working	working	NOUN
cana-1051	130	26	paper	paper	NOUN
cana-1051	130	27	no	no	INTJ
cana-1051	130	28	.	.	PROPN
cana-1051	130	29	197	197	NUM
cana-1051	130	30	,	,	PUNCT
cana-1051	130	31	centre	centre	NOUN
cana-1051	130	32	for	for	ADP
cana-1051	130	33	development	development	NOUN
cana-1051	130	34	economics	economic	NOUN
cana-1051	130	35	,	,	PUNCT
cana-1051	130	36	university	university	NOUN
cana-1051	130	37	of	of	ADP
cana-1051	130	38	delhi	delhi	PROPN
cana-1051	130	39	.	.	PUNCT
cana-1051	131	1	communications	communication	NOUN
cana-1051	131	2	on	on	ADP
cana-1051	131	3	applied	apply	VERB
cana-1051	131	4	nonlinear	nonlinear	ADJ
cana-1051	131	5	analysis	analysis	NOUN
cana-1051	131	6	issn	issn	NOUN
cana-1051	131	7	:	:	PUNCT
cana-1051	131	8	1074	1074	NUM
cana-1051	131	9	-	-	PUNCT
cana-1051	131	10	133x	133x	NUM
cana-1051	131	11	vol	vol	NOUN
cana-1051	131	12	31	31	NUM
cana-1051	131	13	no	no	NOUN
cana-1051	131	14	.	.	PUNCT
cana-1051	132	1	5s	5s	NUM
cana-1051	132	2	(	(	PUNCT
cana-1051	132	3	2024	2024	NUM
cana-1051	132	4	)	)	PUNCT
cana-1051	132	5	311	311	NUM
cana-1051	132	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1051	133	1	[	[	X
cana-1051	133	2	11	11	NUM
cana-1051	133	3	]	]	PUNCT
cana-1051	133	4	r.rama	r.rama	X
cana-1051	133	5	krishna	krishna	PROPN
cana-1051	133	6	&	&	CCONJ
cana-1051	133	7	naveen	naveen	PROPN
cana-1051	133	8	kumar.b	kumar.b	PROPN
cana-1051	133	9	.	.	PUNCT
cana-1051	134	1	2013	2013	NUM
cana-1051	134	2	,	,	PUNCT
cana-1051	134	3	forecasting	forecasting	NOUN
cana-1051	134	4	yield	yield	NOUN
cana-1051	134	5	per	per	ADP
cana-1051	134	6	hectore	hectore	NOUN
cana-1051	134	7	of	of	ADP
cana-1051	134	8	rice	rice	NOUN
cana-1051	134	9	in	in	ADP
cana-1051	134	10	ap	ap	PROPN
cana-1051	134	11	,	,	PUNCT
cana-1051	134	12	ijmcar	ijmcar	PROPN
cana-1051	134	13	,	,	PUNCT
cana-1051	134	14	issn	issn	PROPN
cana-1051	134	15	22496955	22496955	NUM
cana-1051	134	16	,	,	PUNCT
cana-1051	134	17	volume	volume	NOUN
cana-1051	134	18	3	3	NUM
cana-1051	134	19	,	,	PUNCT
cana-1051	134	20	issue	issue	NOUN
cana-1051	134	21	1	1	NUM
cana-1051	134	22	,	,	PUNCT
cana-1051	134	23	march	march	PROPN
cana-1051	134	24	2013	2013	NUM
cana-1051	134	25	,	,	PUNCT
cana-1051	134	26	9	9	NUM
cana-1051	134	27	-	-	SYM
cana-1051	134	28	14	14	NUM
cana-1051	134	29	.	.	PUNCT
cana-1051	135	1	[	[	X
cana-1051	135	2	12	12	NUM
cana-1051	135	3	]	]	X
cana-1051	135	4	venugopala	venugopala	NOUN
cana-1051	135	5	rao	rao	PROPN
cana-1051	135	6	manneni	manneni	PROPN
cana-1051	135	7	,	,	PUNCT
cana-1051	135	8	naveen	naveen	PROPN
cana-1051	135	9	kumar	kumar	PROPN
cana-1051	135	10	boiroju	boiroju	PROPN
cana-1051	135	11	and	and	CCONJ
cana-1051	135	12	m.krishna	m.krishna	NOUN
cana-1051	135	13	reddy	reddy	PROPN
cana-1051	135	14	(	(	PUNCT
cana-1051	135	15	2011	2011	NUM
cana-1051	135	16	)	)	PUNCT
cana-1051	135	17	,	,	PUNCT
cana-1051	135	18	classification	classification	NOUN
cana-1051	135	19	using	use	VERB
cana-1051	135	20	feed	feed	NOUN
cana-1051	135	21	forward	forward	ADV
cana-1051	135	22	neural	neural	ADJ
cana-1051	135	23	network	network	NOUN
cana-1051	135	24	,	,	PUNCT
cana-1051	135	25	international	international	ADJ
cana-1051	135	26	j.	j.	PROPN
cana-1051	135	27	of	of	ADP
cana-1051	135	28	math	math	PROPN
cana-1051	135	29	.	.	PUNCT
cana-1051	136	1	sci	sci	PROPN
cana-1051	136	2	.	.	PROPN
cana-1051	136	3	&	&	CCONJ
cana-1051	136	4	engg	engg	PROPN
cana-1051	136	5	.	.	PUNCT
cana-1051	137	1	appls	appls	PROPN
cana-1051	137	2	.	.	PUNCT
cana-1051	138	1	(	(	PUNCT
cana-1051	138	2	ijmsea	ijmsea	NOUN
cana-1051	138	3	)	)	PUNCT
cana-1051	138	4	,	,	PUNCT
cana-1051	138	5	vol.5	vol.5	X
cana-1051	138	6	(	(	PUNCT
cana-1051	138	7	6	6	NUM
cana-1051	138	8	)	)	PUNCT
cana-1051	138	9	,	,	PUNCT
cana-1051	138	10	223	223	NUM
cana-1051	138	11	-	-	SYM
cana-1051	138	12	227	227	NUM
cana-1051	138	13	.	.	PUNCT
cana-1051	139	1	[	[	X
cana-1051	139	2	13	13	NUM
cana-1051	139	3	]	]	SYM
cana-1051	139	4	zhang	zhang	PROPN
cana-1051	139	5	,	,	PUNCT
cana-1051	139	6	g.	g.	PROPN
cana-1051	139	7	,	,	PUNCT
cana-1051	139	8	patuwo	patuwo	NOUN
cana-1051	139	9	,	,	PUNCT
cana-1051	139	10	b.e	b.e	PROPN
cana-1051	139	11	.	.	PROPN
cana-1051	139	12	and	and	CCONJ
cana-1051	139	13	hu	hu	PROPN
cana-1051	139	14	,	,	PUNCT
cana-1051	139	15	m.y	m.y	PROPN
cana-1051	139	16	.	.	PROPN
cana-1051	139	17	(	(	PUNCT
cana-1051	139	18	1998	1998	NUM
cana-1051	139	19	)	)	PUNCT
cana-1051	139	20	,	,	PUNCT
cana-1051	139	21	forecasting	forecast	VERB
cana-1051	139	22	with	with	ADP
cana-1051	139	23	artificial	artificial	ADJ
cana-1051	139	24	neural	neural	ADJ
cana-1051	139	25	networks	network	NOUN
cana-1051	139	26	:	:	PUNCT
cana-1051	139	27	the	the	DET
cana-1051	139	28	state	state	NOUN
cana-1051	139	29	of	of	ADP
cana-1051	139	30	the	the	DET
cana-1051	139	31	art	art	NOUN
cana-1051	139	32	,	,	PUNCT
cana-1051	139	33	international	international	ADJ
cana-1051	139	34	journal	journal	NOUN
cana-1051	139	35	of	of	ADP
cana-1051	139	36	forecasting	forecasting	NOUN
cana-1051	139	37	,	,	PUNCT
cana-1051	139	38	14	14	NUM
cana-1051	139	39	,	,	PUNCT
cana-1051	139	40	35	35	NUM
cana-1051	139	41	-	-	SYM
cana-1051	139	42	62	62	NUM
cana-1051	139	43	.	.	PUNCT