id	sid	tid	token	lemma	pos
ajst-32542	1	1	88	88	NUM
ajst-32542	1	2	research	research	NOUN
ajst-32542	1	3	on	on	ADP
ajst-32542	1	4	gold	gold	NOUN
ajst-32542	1	5	price	price	NOUN
ajst-32542	1	6	prediction	prediction	NOUN
ajst-32542	1	7	model	model	NOUN
ajst-32542	1	8	based	base	VERB
ajst-32542	1	9	on	on	ADP
ajst-32542	1	10	ceemdanxgboost	ceemdanxgboost	PROPN
ajst-32542	1	11	xin	xin	PROPN
ajst-32542	1	12	xie	xie	PROPN
ajst-32542	1	13	central	central	PROPN
ajst-32542	1	14	south	south	PROPN
ajst-32542	1	15	university	university	PROPN
ajst-32542	1	16	,	,	PUNCT
ajst-32542	1	17	changsha	changsha	PROPN
ajst-32542	1	18	,	,	PUNCT
ajst-32542	1	19	hunan	hunan	PROPN
ajst-32542	1	20	,	,	PUNCT
ajst-32542	1	21	china	china	PROPN
ajst-32542	1	22	xiexin666xin@163.com	xiexin666xin@163.com	PROPN
ajst-32542	1	23	abstract	abstract	PROPN
ajst-32542	1	24	.	.	PUNCT
ajst-32542	2	1	this	this	DET
ajst-32542	2	2	study	study	NOUN
ajst-32542	2	3	proposes	propose	VERB
ajst-32542	2	4	a	a	DET
ajst-32542	2	5	hybrid	hybrid	ADJ
ajst-32542	2	6	model	model	NOUN
ajst-32542	2	7	that	that	PRON
ajst-32542	2	8	combines	combine	VERB
ajst-32542	2	9	complete	complete	ADJ
ajst-32542	2	10	ensemble	ensemble	ADJ
ajst-32542	2	11	empirical	empirical	ADJ
ajst-32542	2	12	mode	mode	NOUN
ajst-32542	2	13	decomposition	decomposition	NOUN
ajst-32542	2	14	with	with	ADP
ajst-32542	2	15	adaptive	adaptive	ADJ
ajst-32542	2	16	noise	noise	NOUN
ajst-32542	2	17	(	(	PUNCT
ajst-32542	2	18	ceemdan	ceemdan	ADJ
ajst-32542	2	19	)	)	PUNCT
ajst-32542	2	20	and	and	CCONJ
ajst-32542	2	21	extreme	extreme	ADJ
ajst-32542	2	22	gradient	gradient	NOUN
ajst-32542	2	23	boosting	boost	VERB
ajst-32542	2	24	(	(	PUNCT
ajst-32542	2	25	xgboost	xgboost	ADV
ajst-32542	2	26	)	)	PUNCT
ajst-32542	2	27	for	for	ADP
ajst-32542	2	28	predicting	predict	VERB
ajst-32542	2	29	the	the	DET
ajst-32542	2	30	time	time	NOUN
ajst-32542	2	31	series	series	NOUN
ajst-32542	2	32	of	of	ADP
ajst-32542	2	33	gold	gold	NOUN
ajst-32542	2	34	prices	price	NOUN
ajst-32542	2	35	.	.	PUNCT
ajst-32542	3	1	addressing	address	VERB
ajst-32542	3	2	the	the	DET
ajst-32542	3	3	performance	performance	NOUN
ajst-32542	3	4	bottlenecks	bottleneck	NOUN
ajst-32542	3	5	of	of	ADP
ajst-32542	3	6	traditional	traditional	ADJ
ajst-32542	3	7	xgboost	xgboost	ADV
ajst-32542	4	1	when	when	SCONJ
ajst-32542	4	2	dealing	deal	VERB
ajst-32542	4	3	with	with	ADP
ajst-32542	4	4	nonlinear	nonlinear	ADJ
ajst-32542	4	5	and	and	CCONJ
ajst-32542	4	6	non	non	ADJ
ajst-32542	4	7	-	-	ADJ
ajst-32542	4	8	stationary	stationary	ADJ
ajst-32542	4	9	signals	signal	NOUN
ajst-32542	4	10	,	,	PUNCT
ajst-32542	4	11	this	this	DET
ajst-32542	4	12	research	research	NOUN
ajst-32542	4	13	first	first	ADV
ajst-32542	4	14	applies	apply	VERB
ajst-32542	4	15	ceemdan	ceemdan	ADJ
ajst-32542	4	16	to	to	PART
ajst-32542	4	17	decompose	decompose	VERB
ajst-32542	4	18	historical	historical	ADJ
ajst-32542	4	19	gold	gold	NOUN
ajst-32542	4	20	price	price	NOUN
ajst-32542	4	21	data	datum	NOUN
ajst-32542	4	22	into	into	ADP
ajst-32542	4	23	multiscale	multiscale	ADJ
ajst-32542	4	24	signals	signal	NOUN
ajst-32542	4	25	,	,	PUNCT
ajst-32542	4	26	extracting	extract	VERB
ajst-32542	4	27	intrinsic	intrinsic	ADJ
ajst-32542	4	28	mode	mode	NOUN
ajst-32542	4	29	functions	function	NOUN
ajst-32542	4	30	(	(	PUNCT
ajst-32542	4	31	imfs	imfs	NOUN
ajst-32542	4	32	)	)	PUNCT
ajst-32542	4	33	of	of	ADP
ajst-32542	4	34	different	different	ADJ
ajst-32542	4	35	frequency	frequency	NOUN
ajst-32542	4	36	components	component	NOUN
ajst-32542	4	37	.	.	PUNCT
ajst-32542	5	1	these	these	DET
ajst-32542	5	2	imfs	imfs	NOUN
ajst-32542	5	3	are	be	AUX
ajst-32542	5	4	then	then	ADV
ajst-32542	5	5	used	use	VERB
ajst-32542	5	6	as	as	ADP
ajst-32542	5	7	input	input	NOUN
ajst-32542	5	8	features	feature	NOUN
ajst-32542	5	9	for	for	ADP
ajst-32542	5	10	training	training	NOUN
ajst-32542	5	11	and	and	CCONJ
ajst-32542	5	12	prediction	prediction	NOUN
ajst-32542	5	13	with	with	ADP
ajst-32542	5	14	the	the	DET
ajst-32542	5	15	xgboost	xgboost	PROPN
ajst-32542	5	16	model	model	PROPN
ajst-32542	5	17	.	.	PUNCT
ajst-32542	6	1	experimental	experimental	ADJ
ajst-32542	6	2	results	result	NOUN
ajst-32542	6	3	show	show	VERB
ajst-32542	6	4	that	that	SCONJ
ajst-32542	6	5	the	the	DET
ajst-32542	6	6	ceemdanxgboost	ceemdanxgboost	ADJ
ajst-32542	6	7	model	model	NOUN
ajst-32542	6	8	achieves	achieve	VERB
ajst-32542	6	9	high	high	ADJ
ajst-32542	6	10	prediction	prediction	NOUN
ajst-32542	6	11	accuracy	accuracy	NOUN
ajst-32542	6	12	on	on	ADP
ajst-32542	6	13	both	both	CCONJ
ajst-32542	6	14	the	the	DET
ajst-32542	6	15	training	training	NOUN
ajst-32542	6	16	and	and	CCONJ
ajst-32542	6	17	testing	testing	NOUN
ajst-32542	6	18	sets	set	NOUN
ajst-32542	6	19	,	,	PUNCT
ajst-32542	6	20	outperforming	outperform	VERB
ajst-32542	6	21	the	the	DET
ajst-32542	6	22	standalone	standalone	NOUN
ajst-32542	6	23	xgboost	xgboost	NOUN
ajst-32542	6	24	model	model	NOUN
ajst-32542	6	25	in	in	ADP
ajst-32542	6	26	terms	term	NOUN
ajst-32542	6	27	of	of	ADP
ajst-32542	6	28	generalization	generalization	NOUN
ajst-32542	6	29	ability	ability	NOUN
ajst-32542	6	30	and	and	CCONJ
ajst-32542	6	31	prediction	prediction	NOUN
ajst-32542	6	32	performance	performance	NOUN
ajst-32542	6	33	.	.	PUNCT
ajst-32542	7	1	this	this	DET
ajst-32542	7	2	study	study	NOUN
ajst-32542	7	3	not	not	PART
ajst-32542	7	4	only	only	ADV
ajst-32542	7	5	provides	provide	VERB
ajst-32542	7	6	an	an	DET
ajst-32542	7	7	effective	effective	ADJ
ajst-32542	7	8	modeling	modeling	NOUN
ajst-32542	7	9	approach	approach	NOUN
ajst-32542	7	10	for	for	ADP
ajst-32542	7	11	gold	gold	NOUN
ajst-32542	7	12	price	price	NOUN
ajst-32542	7	13	prediction	prediction	NOUN
ajst-32542	7	14	but	but	CCONJ
ajst-32542	7	15	also	also	ADV
ajst-32542	7	16	offers	offer	VERB
ajst-32542	7	17	new	new	ADJ
ajst-32542	7	18	insights	insight	NOUN
ajst-32542	7	19	for	for	ADP
ajst-32542	7	20	handling	handle	VERB
ajst-32542	7	21	nonlinear	nonlinear	ADJ
ajst-32542	7	22	and	and	CCONJ
ajst-32542	7	23	non	non	ADJ
ajst-32542	7	24	-	-	ADJ
ajst-32542	7	25	stationary	stationary	ADJ
ajst-32542	7	26	features	feature	NOUN
ajst-32542	7	27	in	in	ADP
ajst-32542	7	28	other	other	ADJ
ajst-32542	7	29	financial	financial	ADJ
ajst-32542	7	30	time	time	NOUN
ajst-32542	7	31	series	series	PROPN
ajst-32542	7	32	data	data	PROPN
ajst-32542	7	33	.	.	PUNCT
ajst-32542	8	1	keywords	keyword	NOUN
ajst-32542	8	2	:	:	PUNCT
ajst-32542	8	3	gold	gold	NOUN
ajst-32542	8	4	price	price	NOUN
ajst-32542	8	5	prediction	prediction	NOUN
ajst-32542	8	6	;	;	PUNCT
ajst-32542	8	7	ceemdan	ceemdan	PROPN
ajst-32542	8	8	;	;	PUNCT
ajst-32542	8	9	xgboost	xgboost	NUM
ajst-32542	8	10	;	;	PUNCT
ajst-32542	8	11	time	time	NOUN
ajst-32542	8	12	series	series	PROPN
ajst-32542	8	13	analysis	analysis	NOUN
ajst-32542	8	14	;	;	PUNCT
ajst-32542	8	15	machine	machine	NOUN
ajst-32542	8	16	learning	learning	NOUN
ajst-32542	8	17	.	.	PUNCT
ajst-32542	9	1	1	1	X
ajst-32542	9	2	.	.	X
ajst-32542	9	3	introduction	introduction	NOUN
ajst-32542	9	4	gold	gold	NOUN
ajst-32542	9	5	,	,	PUNCT
ajst-32542	9	6	as	as	ADP
ajst-32542	9	7	one	one	NUM
ajst-32542	9	8	of	of	ADP
ajst-32542	9	9	the	the	DET
ajst-32542	9	10	most	most	ADV
ajst-32542	9	11	important	important	ADJ
ajst-32542	9	12	global	global	ADJ
ajst-32542	9	13	financial	financial	ADJ
ajst-32542	9	14	assets	asset	NOUN
ajst-32542	9	15	,	,	PUNCT
ajst-32542	9	16	has	have	AUX
ajst-32542	9	17	long	long	ADV
ajst-32542	9	18	been	be	AUX
ajst-32542	9	19	considered	consider	VERB
ajst-32542	9	20	a	a	DET
ajst-32542	9	21	safe	safe	ADJ
ajst-32542	9	22	-	-	PUNCT
ajst-32542	9	23	haven	haven	NOUN
ajst-32542	9	24	tool	tool	NOUN
ajst-32542	9	25	against	against	ADP
ajst-32542	9	26	inflation	inflation	NOUN
ajst-32542	9	27	and	and	CCONJ
ajst-32542	9	28	economic	economic	ADJ
ajst-32542	9	29	uncertainty	uncertainty	NOUN
ajst-32542	9	30	.	.	PUNCT
ajst-32542	10	1	its	its	PRON
ajst-32542	10	2	price	price	NOUN
ajst-32542	10	3	fluctuations	fluctuation	NOUN
ajst-32542	10	4	not	not	PART
ajst-32542	10	5	only	only	ADV
ajst-32542	10	6	reflect	reflect	VERB
ajst-32542	10	7	the	the	DET
ajst-32542	10	8	macroeconomic	macroeconomic	ADJ
ajst-32542	10	9	conditions	condition	NOUN
ajst-32542	10	10	but	but	CCONJ
ajst-32542	10	11	also	also	ADV
ajst-32542	10	12	have	have	VERB
ajst-32542	10	13	significant	significant	ADJ
ajst-32542	10	14	impacts	impact	NOUN
ajst-32542	10	15	on	on	ADP
ajst-32542	10	16	investor	investor	NOUN
ajst-32542	10	17	behavior	behavior	NOUN
ajst-32542	10	18	,	,	PUNCT
ajst-32542	10	19	monetary	monetary	ADJ
ajst-32542	10	20	policy	policy	NOUN
ajst-32542	10	21	formulation	formulation	NOUN
ajst-32542	10	22	,	,	PUNCT
ajst-32542	10	23	and	and	CCONJ
ajst-32542	10	24	financial	financial	ADJ
ajst-32542	10	25	market	market	NOUN
ajst-32542	10	26	stability[1	stability[1	NOUN
ajst-32542	10	27	]	]	PUNCT
ajst-32542	10	28	.	.	PUNCT
ajst-32542	11	1	gold	gold	NOUN
ajst-32542	11	2	prices	price	NOUN
ajst-32542	11	3	are	be	AUX
ajst-32542	11	4	influenced	influence	VERB
ajst-32542	11	5	by	by	ADP
ajst-32542	11	6	multiple	multiple	ADJ
ajst-32542	11	7	factors	factor	NOUN
ajst-32542	11	8	,	,	PUNCT
ajst-32542	11	9	including	include	VERB
ajst-32542	11	10	the	the	DET
ajst-32542	11	11	us	us	PROPN
ajst-32542	11	12	dollar	dollar	NOUN
ajst-32542	11	13	exchange	exchange	NOUN
ajst-32542	11	14	rate	rate	NOUN
ajst-32542	11	15	,	,	PUNCT
ajst-32542	11	16	oil	oil	NOUN
ajst-32542	11	17	prices	price	NOUN
ajst-32542	11	18	,	,	PUNCT
ajst-32542	11	19	interest	interest	NOUN
ajst-32542	11	20	rates	rate	NOUN
ajst-32542	11	21	,	,	PUNCT
ajst-32542	11	22	stock	stock	NOUN
ajst-32542	11	23	market	market	NOUN
ajst-32542	11	24	volatility	volatility	NOUN
ajst-32542	11	25	,	,	PUNCT
ajst-32542	11	26	and	and	CCONJ
ajst-32542	11	27	geopolitical	geopolitical	ADJ
ajst-32542	11	28	risks	risk	NOUN
ajst-32542	11	29	,	,	PUNCT
ajst-32542	11	30	among	among	ADP
ajst-32542	11	31	other	other	ADJ
ajst-32542	11	32	complex	complex	ADJ
ajst-32542	11	33	variables	variable	NOUN
ajst-32542	11	34	,	,	PUNCT
ajst-32542	11	35	making	make	VERB
ajst-32542	11	36	gold	gold	NOUN
ajst-32542	11	37	price	price	NOUN
ajst-32542	11	38	forecasting	forecast	VERB
ajst-32542	11	39	a	a	DET
ajst-32542	11	40	core	core	NOUN
ajst-32542	11	41	issue	issue	NOUN
ajst-32542	11	42	in	in	ADP
ajst-32542	11	43	financial	financial	ADJ
ajst-32542	11	44	research	research	NOUN
ajst-32542	11	45	and	and	CCONJ
ajst-32542	11	46	investment	investment	NOUN
ajst-32542	11	47	decision	decision	NOUN
ajst-32542	11	48	-	-	PUNCT
ajst-32542	11	49	making[2	making[2	PRON
ajst-32542	11	50	]	]	PUNCT
ajst-32542	11	51	.	.	PUNCT
ajst-32542	12	1	traditional	traditional	ADJ
ajst-32542	12	2	statistical	statistical	ADJ
ajst-32542	12	3	models	model	NOUN
ajst-32542	12	4	,	,	PUNCT
ajst-32542	12	5	such	such	ADJ
ajst-32542	12	6	as	as	ADP
ajst-32542	12	7	autoregressive	autoregressive	ADJ
ajst-32542	12	8	integrated	integrated	ADJ
ajst-32542	12	9	moving	move	VERB
ajst-32542	12	10	average	average	ADJ
ajst-32542	12	11	(	(	PUNCT
ajst-32542	12	12	arima	arima	NOUN
ajst-32542	12	13	)	)	PUNCT
ajst-32542	12	14	and	and	CCONJ
ajst-32542	12	15	generalized	generalize	VERB
ajst-32542	12	16	autoregressive	autoregressive	ADJ
ajst-32542	12	17	conditional	conditional	ADJ
ajst-32542	12	18	heteroskedasticity	heteroskedasticity	NOUN
ajst-32542	12	19	(	(	PUNCT
ajst-32542	12	20	garch	garch	NOUN
ajst-32542	12	21	)	)	PUNCT
ajst-32542	12	22	,	,	PUNCT
ajst-32542	12	23	face	face	VERB
ajst-32542	12	24	limitations	limitation	NOUN
ajst-32542	12	25	in	in	ADP
ajst-32542	12	26	handling	handle	VERB
ajst-32542	12	27	nonlinear	nonlinear	ADJ
ajst-32542	12	28	and	and	CCONJ
ajst-32542	12	29	non	non	ADJ
ajst-32542	12	30	-	-	ADJ
ajst-32542	12	31	stationary	stationary	ADJ
ajst-32542	12	32	time	time	NOUN
ajst-32542	12	33	series	series	NOUN
ajst-32542	12	34	,	,	PUNCT
ajst-32542	12	35	making	make	VERB
ajst-32542	12	36	it	it	PRON
ajst-32542	12	37	difficult	difficult	ADJ
ajst-32542	12	38	to	to	PART
ajst-32542	12	39	capture	capture	VERB
ajst-32542	12	40	the	the	DET
ajst-32542	12	41	complex	complex	ADJ
ajst-32542	12	42	dynamic	dynamic	ADJ
ajst-32542	12	43	relationships	relationship	NOUN
ajst-32542	12	44	among	among	ADP
ajst-32542	12	45	economic	economic	ADJ
ajst-32542	12	46	variables	variable	NOUN
ajst-32542	12	47	,	,	PUNCT
ajst-32542	12	48	leading	lead	VERB
ajst-32542	12	49	to	to	ADP
ajst-32542	12	50	insufficient	insufficient	ADJ
ajst-32542	12	51	prediction	prediction	NOUN
ajst-32542	12	52	accuracy	accuracy	NOUN
ajst-32542	12	53	.	.	PUNCT
ajst-32542	13	1	with	with	ADP
ajst-32542	13	2	the	the	DET
ajst-32542	13	3	rapid	rapid	ADJ
ajst-32542	13	4	development	development	NOUN
ajst-32542	13	5	of	of	ADP
ajst-32542	13	6	artificial	artificial	ADJ
ajst-32542	13	7	intelligence	intelligence	NOUN
ajst-32542	13	8	and	and	CCONJ
ajst-32542	13	9	big	big	ADJ
ajst-32542	13	10	data	datum	NOUN
ajst-32542	13	11	technologies	technology	NOUN
ajst-32542	13	12	,	,	PUNCT
ajst-32542	13	13	machine	machine	NOUN
ajst-32542	13	14	learning	learning	NOUN
ajst-32542	13	15	(	(	PUNCT
ajst-32542	13	16	ml	ml	NOUN
ajst-32542	13	17	)	)	PUNCT
ajst-32542	13	18	and	and	CCONJ
ajst-32542	13	19	deep	deep	ADJ
ajst-32542	13	20	learning	learning	NOUN
ajst-32542	13	21	(	(	PUNCT
ajst-32542	13	22	dl	dl	NOUN
ajst-32542	13	23	)	)	PUNCT
ajst-32542	13	24	methods	method	NOUN
ajst-32542	13	25	have	have	AUX
ajst-32542	13	26	been	be	AUX
ajst-32542	13	27	widely	widely	ADV
ajst-32542	13	28	applied	apply	VERB
ajst-32542	13	29	to	to	ADP
ajst-32542	13	30	financial	financial	ADJ
ajst-32542	13	31	market	market	NOUN
ajst-32542	13	32	prediction	prediction	NOUN
ajst-32542	13	33	tasks	task	NOUN
ajst-32542	13	34	,	,	PUNCT
ajst-32542	13	35	offering	offer	VERB
ajst-32542	13	36	new	new	ADJ
ajst-32542	13	37	possibilities	possibility	NOUN
ajst-32542	13	38	for	for	ADP
ajst-32542	13	39	modeling	model	VERB
ajst-32542	13	40	complex	complex	ADJ
ajst-32542	13	41	time	time	NOUN
ajst-32542	13	42	series	series	NOUN
ajst-32542	13	43	data	datum	NOUN
ajst-32542	13	44	and	and	CCONJ
ajst-32542	13	45	extracting	extract	VERB
ajst-32542	13	46	nonlinear	nonlinear	ADJ
ajst-32542	13	47	features	feature	NOUN
ajst-32542	13	48	.	.	PUNCT
ajst-32542	14	1	these	these	DET
ajst-32542	14	2	methods	method	NOUN
ajst-32542	14	3	can	can	AUX
ajst-32542	14	4	automatically	automatically	ADV
ajst-32542	14	5	learn	learn	VERB
ajst-32542	14	6	latent	latent	NOUN
ajst-32542	14	7	patterns	pattern	NOUN
ajst-32542	14	8	from	from	ADP
ajst-32542	14	9	historical	historical	ADJ
ajst-32542	14	10	data	datum	NOUN
ajst-32542	14	11	,	,	PUNCT
ajst-32542	14	12	thereby	thereby	ADV
ajst-32542	14	13	effectively	effectively	ADV
ajst-32542	14	14	improving	improve	VERB
ajst-32542	14	15	the	the	DET
ajst-32542	14	16	accuracy	accuracy	NOUN
ajst-32542	14	17	and	and	CCONJ
ajst-32542	14	18	stability	stability	NOUN
ajst-32542	14	19	of	of	ADP
ajst-32542	14	20	gold	gold	ADJ
ajst-32542	14	21	price	price	NOUN
ajst-32542	14	22	predictions[3	predictions[3	NOUN
ajst-32542	14	23	]	]	PUNCT
ajst-32542	14	24	.	.	PUNCT
ajst-32542	15	1	in	in	ADP
ajst-32542	15	2	related	related	ADJ
ajst-32542	15	3	studies	study	NOUN
ajst-32542	15	4	,	,	PUNCT
ajst-32542	15	5	traditional	traditional	ADJ
ajst-32542	15	6	machine	machine	NOUN
ajst-32542	15	7	learning	learning	NOUN
ajst-32542	15	8	methods	method	NOUN
ajst-32542	15	9	such	such	ADJ
ajst-32542	15	10	as	as	ADP
ajst-32542	15	11	linear	linear	PROPN
ajst-32542	15	12	regression	regression	NOUN
ajst-32542	15	13	(	(	PUNCT
ajst-32542	15	14	lr	lr	NOUN
ajst-32542	15	15	)	)	PUNCT
ajst-32542	15	16	,	,	PUNCT
ajst-32542	15	17	support	support	VERB
ajst-32542	15	18	vector	vector	NOUN
ajst-32542	15	19	machines	machine	NOUN
ajst-32542	15	20	(	(	PUNCT
ajst-32542	15	21	svm	svm	PROPN
ajst-32542	15	22	)	)	PUNCT
ajst-32542	15	23	,	,	PUNCT
ajst-32542	15	24	random	random	ADJ
ajst-32542	15	25	forest	forest	NOUN
ajst-32542	15	26	(	(	PUNCT
ajst-32542	15	27	rf	rf	NOUN
ajst-32542	15	28	)	)	PUNCT
ajst-32542	15	29	,	,	PUNCT
ajst-32542	15	30	and	and	CCONJ
ajst-32542	15	31	gradient	gradient	NOUN
ajst-32542	15	32	boosting	boost	VERB
ajst-32542	15	33	trees	tree	NOUN
ajst-32542	15	34	(	(	PUNCT
ajst-32542	15	35	gbt	gbt	PROPN
ajst-32542	15	36	)	)	PUNCT
ajst-32542	15	37	have	have	AUX
ajst-32542	15	38	been	be	AUX
ajst-32542	15	39	successfully	successfully	ADV
ajst-32542	15	40	used	use	VERB
ajst-32542	15	41	for	for	ADP
ajst-32542	15	42	gold	gold	NOUN
ajst-32542	15	43	price	price	NOUN
ajst-32542	15	44	prediction[4	prediction[4	NOUN
ajst-32542	15	45	]	]	PUNCT
ajst-32542	15	46	.	.	PUNCT
ajst-32542	16	1	for	for	ADP
ajst-32542	16	2	instance	instance	NOUN
ajst-32542	16	3	,	,	PUNCT
ajst-32542	16	4	gradient	gradient	ADJ
ajst-32542	16	5	boosting	boost	VERB
ajst-32542	16	6	regression	regression	NOUN
ajst-32542	16	7	models	model	NOUN
ajst-32542	16	8	constructed	construct	VERB
ajst-32542	16	9	using	use	VERB
ajst-32542	16	10	macroeconomic	macroeconomic	ADJ
ajst-32542	16	11	variables	variable	NOUN
ajst-32542	16	12	such	such	ADJ
ajst-32542	16	13	as	as	ADP
ajst-32542	16	14	the	the	DET
ajst-32542	16	15	us	us	PROPN
ajst-32542	16	16	dollar	dollar	NOUN
ajst-32542	16	17	index	index	NOUN
ajst-32542	16	18	,	,	PUNCT
ajst-32542	16	19	crude	crude	ADJ
ajst-32542	16	20	oil	oil	NOUN
ajst-32542	16	21	prices	price	NOUN
ajst-32542	16	22	,	,	PUNCT
ajst-32542	16	23	and	and	CCONJ
ajst-32542	16	24	the	the	DET
ajst-32542	16	25	s&p	s&p	PROPN
ajst-32542	16	26	500	500	NUM
ajst-32542	16	27	index	index	NOUN
ajst-32542	16	28	have	have	AUX
ajst-32542	16	29	achieved	achieve	VERB
ajst-32542	16	30	high	high	ADJ
ajst-32542	16	31	prediction	prediction	NOUN
ajst-32542	16	32	accuracy[5	accuracy[5	PROPN
ajst-32542	16	33	]	]	X
ajst-32542	16	34	.	.	PUNCT
ajst-32542	17	1	random	random	ADJ
ajst-32542	17	2	forest	forest	NOUN
ajst-32542	17	3	-	-	PUNCT
ajst-32542	17	4	based	base	VERB
ajst-32542	17	5	analysis	analysis	NOUN
ajst-32542	17	6	of	of	ADP
ajst-32542	17	7	22	22	NUM
ajst-32542	17	8	market	market	NOUN
ajst-32542	17	9	variables	variable	NOUN
ajst-32542	17	10	'	'	PART
ajst-32542	17	11	impact	impact	NOUN
ajst-32542	17	12	on	on	ADP
ajst-32542	17	13	gold	gold	NOUN
ajst-32542	17	14	prices	price	NOUN
ajst-32542	17	15	further	far	ADV
ajst-32542	17	16	validated	validate	VERB
ajst-32542	17	17	the	the	DET
ajst-32542	17	18	applicability	applicability	NOUN
ajst-32542	17	19	of	of	ADP
ajst-32542	17	20	machine	machine	NOUN
ajst-32542	17	21	learning	learning	NOUN
ajst-32542	17	22	models	model	NOUN
ajst-32542	17	23	in	in	ADP
ajst-32542	17	24	complex	complex	ADJ
ajst-32542	17	25	market	market	NOUN
ajst-32542	17	26	environments[6	environments[6	ADV
ajst-32542	17	27	]	]	PUNCT
ajst-32542	17	28	.	.	PUNCT
ajst-32542	18	1	in	in	ADP
ajst-32542	18	2	contrast	contrast	NOUN
ajst-32542	18	3	,	,	PUNCT
ajst-32542	18	4	deep	deep	ADJ
ajst-32542	18	5	learning	learning	NOUN
ajst-32542	18	6	models	model	NOUN
ajst-32542	18	7	,	,	PUNCT
ajst-32542	18	8	with	with	ADP
ajst-32542	18	9	their	their	PRON
ajst-32542	18	10	superior	superior	ADJ
ajst-32542	18	11	feature	feature	NOUN
ajst-32542	18	12	representation	representation	NOUN
ajst-32542	18	13	and	and	CCONJ
ajst-32542	18	14	temporal	temporal	ADJ
ajst-32542	18	15	dependency	dependency	NOUN
ajst-32542	18	16	capture	capture	NOUN
ajst-32542	18	17	capabilities	capability	NOUN
ajst-32542	18	18	,	,	PUNCT
ajst-32542	18	19	have	have	AUX
ajst-32542	18	20	seen	see	VERB
ajst-32542	18	21	increased	increase	VERB
ajst-32542	18	22	applications	application	NOUN
ajst-32542	18	23	in	in	ADP
ajst-32542	18	24	gold	gold	NOUN
ajst-32542	18	25	price	price	NOUN
ajst-32542	18	26	prediction	prediction	NOUN
ajst-32542	18	27	.	.	PUNCT
ajst-32542	19	1	a	a	DET
ajst-32542	19	2	comparison	comparison	NOUN
ajst-32542	19	3	between	between	ADP
ajst-32542	19	4	long	long	ADJ
ajst-32542	19	5	short	short	ADJ
ajst-32542	19	6	-	-	PUNCT
ajst-32542	19	7	term	term	NOUN
ajst-32542	19	8	memory	memory	NOUN
ajst-32542	19	9	(	(	PUNCT
ajst-32542	19	10	lstm	lstm	NOUN
ajst-32542	19	11	)	)	PUNCT
ajst-32542	19	12	and	and	CCONJ
ajst-32542	19	13	gated	gate	VERB
ajst-32542	19	14	recurrent	recurrent	ADJ
ajst-32542	19	15	unit	unit	NOUN
ajst-32542	19	16	(	(	PUNCT
ajst-32542	19	17	gru	gru	NOUN
ajst-32542	19	18	)	)	PUNCT
ajst-32542	19	19	models	model	NOUN
ajst-32542	19	20	in	in	ADP
ajst-32542	19	21	the	the	DET
ajst-32542	19	22	indonesian	indonesian	ADJ
ajst-32542	19	23	gold	gold	NOUN
ajst-32542	19	24	market	market	NOUN
ajst-32542	19	25	found	find	VERB
ajst-32542	19	26	that	that	SCONJ
ajst-32542	19	27	the	the	DET
ajst-32542	19	28	gru	gru	NOUN
ajst-32542	19	29	model	model	NOUN
ajst-32542	19	30	significantly	significantly	ADV
ajst-32542	19	31	outperforms	outperform	VERB
ajst-32542	19	32	traditional	traditional	ADJ
ajst-32542	19	33	regression	regression	NOUN
ajst-32542	19	34	methods	method	NOUN
ajst-32542	19	35	in	in	ADP
ajst-32542	19	36	terms	term	NOUN
ajst-32542	19	37	of	of	ADP
ajst-32542	19	38	prediction	prediction	NOUN
ajst-32542	19	39	accuracy[7	accuracy[7	PROPN
ajst-32542	19	40	]	]	PUNCT
ajst-32542	19	41	.	.	PUNCT
ajst-32542	20	1	a	a	DET
ajst-32542	20	2	two	two	NUM
ajst-32542	20	3	-	-	PUNCT
ajst-32542	20	4	stage	stage	NOUN
ajst-32542	20	5	deep	deep	ADJ
ajst-32542	20	6	fusion	fusion	NOUN
ajst-32542	20	7	framework	framework	NOUN
ajst-32542	20	8	that	that	PRON
ajst-32542	20	9	combines	combine	VERB
ajst-32542	20	10	feature	feature	NOUN
ajst-32542	20	11	fusion	fusion	NOUN
ajst-32542	20	12	and	and	CCONJ
ajst-32542	20	13	residual	residual	ADJ
ajst-32542	20	14	correction	correction	NOUN
ajst-32542	20	15	effectively	effectively	ADV
ajst-32542	20	16	improves	improve	VERB
ajst-32542	20	17	the	the	DET
ajst-32542	20	18	89	89	NUM
ajst-32542	20	19	stability	stability	NOUN
ajst-32542	20	20	and	and	CCONJ
ajst-32542	20	21	accuracy	accuracy	NOUN
ajst-32542	20	22	of	of	ADP
ajst-32542	20	23	multi	multi	ADJ
ajst-32542	20	24	-	-	ADJ
ajst-32542	20	25	market	market	ADJ
ajst-32542	20	26	gold	gold	NOUN
ajst-32542	20	27	price	price	NOUN
ajst-32542	20	28	predictions[8	predictions[8	PROPN
ajst-32542	20	29	]	]	X
ajst-32542	20	30	.	.	PUNCT
ajst-32542	21	1	moreover	moreover	ADV
ajst-32542	21	2	,	,	PUNCT
ajst-32542	21	3	ensemble	ensemble	ADJ
ajst-32542	21	4	learning	learning	NOUN
ajst-32542	21	5	and	and	CCONJ
ajst-32542	21	6	hybrid	hybrid	ADJ
ajst-32542	21	7	modeling	modeling	NOUN
ajst-32542	21	8	studies	study	NOUN
ajst-32542	21	9	show	show	VERB
ajst-32542	21	10	that	that	SCONJ
ajst-32542	21	11	heterogeneous	heterogeneous	ADJ
ajst-32542	21	12	ensemble	ensemble	ADJ
ajst-32542	21	13	learning	learning	NOUN
ajst-32542	21	14	frameworks	framework	NOUN
ajst-32542	21	15	that	that	PRON
ajst-32542	21	16	combine	combine	VERB
ajst-32542	21	17	multiple	multiple	ADJ
ajst-32542	21	18	algorithms	algorithm	NOUN
ajst-32542	21	19	can	can	AUX
ajst-32542	21	20	further	far	ADV
ajst-32542	21	21	improve	improve	VERB
ajst-32542	21	22	prediction	prediction	NOUN
ajst-32542	21	23	performance	performance	NOUN
ajst-32542	21	24	and	and	CCONJ
ajst-32542	21	25	model	model	NOUN
ajst-32542	21	26	robustness	robustness	NOUN
ajst-32542	21	27	.	.	PUNCT
ajst-32542	22	1	research	research	NOUN
ajst-32542	22	2	indicates	indicate	VERB
ajst-32542	22	3	that	that	SCONJ
ajst-32542	22	4	multi	multi	ADJ
ajst-32542	22	5	-	-	ADJ
ajst-32542	22	6	layer	layer	ADJ
ajst-32542	22	7	ensemble	ensemble	ADJ
ajst-32542	22	8	models	model	NOUN
ajst-32542	22	9	combining	combine	VERB
ajst-32542	22	10	random	random	ADJ
ajst-32542	22	11	forest	forest	NOUN
ajst-32542	22	12	,	,	PUNCT
ajst-32542	22	13	svm	svm	PROPN
ajst-32542	22	14	,	,	PUNCT
ajst-32542	22	15	gradient	gradient	ADJ
ajst-32542	22	16	boosting	boosting	NOUN
ajst-32542	22	17	,	,	PUNCT
ajst-32542	22	18	and	and	CCONJ
ajst-32542	22	19	lstm	lstm	NOUN
ajst-32542	22	20	achieve	achieve	VERB
ajst-32542	22	21	superior	superior	ADJ
ajst-32542	22	22	prediction	prediction	NOUN
ajst-32542	22	23	results	result	NOUN
ajst-32542	22	24	across	across	ADP
ajst-32542	22	25	multiple	multiple	ADJ
ajst-32542	22	26	metrics	metric	NOUN
ajst-32542	22	27	compared	compare	VERB
ajst-32542	22	28	to	to	ADP
ajst-32542	22	29	single	single	ADJ
ajst-32542	22	30	models[9	models[9	NOUN
ajst-32542	22	31	]	]	PUNCT
ajst-32542	22	32	.	.	PUNCT
ajst-32542	23	1	despite	despite	SCONJ
ajst-32542	23	2	significant	significant	ADJ
ajst-32542	23	3	progress	progress	NOUN
ajst-32542	23	4	in	in	ADP
ajst-32542	23	5	these	these	DET
ajst-32542	23	6	studies	study	NOUN
ajst-32542	23	7	,	,	PUNCT
ajst-32542	23	8	there	there	PRON
ajst-32542	23	9	remain	remain	VERB
ajst-32542	23	10	several	several	ADJ
ajst-32542	23	11	shortcomings	shortcoming	NOUN
ajst-32542	23	12	in	in	ADP
ajst-32542	23	13	the	the	DET
ajst-32542	23	14	current	current	ADJ
ajst-32542	23	15	literature	literature	NOUN
ajst-32542	23	16	.	.	PUNCT
ajst-32542	24	1	first	first	ADV
ajst-32542	24	2	,	,	PUNCT
ajst-32542	24	3	most	most	ADJ
ajst-32542	24	4	studies	study	NOUN
ajst-32542	24	5	focus	focus	VERB
ajst-32542	24	6	only	only	ADV
ajst-32542	24	7	on	on	ADP
ajst-32542	24	8	single	single	ADJ
ajst-32542	24	9	model	model	NOUN
ajst-32542	24	10	structures	structure	NOUN
ajst-32542	24	11	or	or	CCONJ
ajst-32542	24	12	limited	limited	ADJ
ajst-32542	24	13	feature	feature	NOUN
ajst-32542	24	14	sets	set	NOUN
ajst-32542	24	15	,	,	PUNCT
ajst-32542	24	16	failing	fail	VERB
ajst-32542	24	17	to	to	PART
ajst-32542	24	18	adequately	adequately	ADV
ajst-32542	24	19	capture	capture	VERB
ajst-32542	24	20	the	the	DET
ajst-32542	24	21	nonlinear	nonlinear	ADJ
ajst-32542	24	22	interactions	interaction	NOUN
ajst-32542	24	23	and	and	CCONJ
ajst-32542	24	24	temporal	temporal	ADJ
ajst-32542	24	25	dependencies	dependency	NOUN
ajst-32542	24	26	among	among	ADP
ajst-32542	24	27	multi	multi	ADJ
ajst-32542	24	28	-	-	ADJ
ajst-32542	24	29	source	source	NOUN
ajst-32542	24	30	economic	economic	ADJ
ajst-32542	24	31	indicators[10	indicators[10	PROPN
ajst-32542	24	32	]	]	PUNCT
ajst-32542	24	33	.	.	PUNCT
ajst-32542	25	1	secondly	secondly	ADV
ajst-32542	25	2	,	,	PUNCT
ajst-32542	25	3	although	although	SCONJ
ajst-32542	25	4	deep	deep	ADJ
ajst-32542	25	5	learning	learning	NOUN
ajst-32542	25	6	models	model	NOUN
ajst-32542	25	7	have	have	VERB
ajst-32542	25	8	powerful	powerful	ADJ
ajst-32542	25	9	fitting	fitting	ADJ
ajst-32542	25	10	capabilities	capability	NOUN
ajst-32542	25	11	,	,	PUNCT
ajst-32542	25	12	they	they	PRON
ajst-32542	25	13	face	face	VERB
ajst-32542	25	14	risks	risk	NOUN
ajst-32542	25	15	of	of	ADP
ajst-32542	25	16	overfitting	overfitting	NOUN
ajst-32542	25	17	and	and	CCONJ
ajst-32542	25	18	lack	lack	NOUN
ajst-32542	25	19	of	of	ADP
ajst-32542	25	20	interpretability	interpretability	NOUN
ajst-32542	25	21	,	,	PUNCT
ajst-32542	25	22	limiting	limit	VERB
ajst-32542	25	23	their	their	PRON
ajst-32542	25	24	application	application	NOUN
ajst-32542	25	25	in	in	ADP
ajst-32542	25	26	high	high	ADJ
ajst-32542	25	27	-	-	PUNCT
ajst-32542	25	28	risk	risk	NOUN
ajst-32542	25	29	financial	financial	ADJ
ajst-32542	25	30	decision	decision	NOUN
ajst-32542	25	31	-	-	PUNCT
ajst-32542	25	32	making[11	making[11	NOUN
ajst-32542	25	33	]	]	X
ajst-32542	25	34	.	.	PUNCT
ajst-32542	26	1	additionally	additionally	ADV
ajst-32542	26	2	,	,	PUNCT
ajst-32542	26	3	the	the	DET
ajst-32542	26	4	price	price	NOUN
ajst-32542	26	5	linkage	linkage	NOUN
ajst-32542	26	6	effects	effect	NOUN
ajst-32542	26	7	between	between	ADP
ajst-32542	26	8	different	different	ADJ
ajst-32542	26	9	gold	gold	NOUN
ajst-32542	26	10	markets	market	NOUN
ajst-32542	26	11	(	(	PUNCT
ajst-32542	26	12	e.g.	e.g.	ADV
ajst-32542	26	13	,	,	PUNCT
ajst-32542	26	14	london	london	PROPN
ajst-32542	26	15	,	,	PUNCT
ajst-32542	26	16	new	new	PROPN
ajst-32542	26	17	york	york	PROPN
ajst-32542	26	18	,	,	PUNCT
ajst-32542	26	19	shanghai	shanghai	PROPN
ajst-32542	26	20	)	)	PUNCT
ajst-32542	26	21	and	and	CCONJ
ajst-32542	26	22	cross	cross	ADJ
ajst-32542	26	23	-	-	ADJ
ajst-32542	26	24	market	market	ADJ
ajst-32542	26	25	dynamic	dynamic	ADJ
ajst-32542	26	26	features	feature	NOUN
ajst-32542	26	27	have	have	AUX
ajst-32542	26	28	not	not	PART
ajst-32542	26	29	been	be	AUX
ajst-32542	26	30	sufficiently	sufficiently	ADV
ajst-32542	26	31	considered	consider	VERB
ajst-32542	26	32	,	,	PUNCT
ajst-32542	26	33	which	which	PRON
ajst-32542	26	34	undermines	undermine	VERB
ajst-32542	26	35	the	the	DET
ajst-32542	26	36	generalization	generalization	NOUN
ajst-32542	26	37	performance	performance	NOUN
ajst-32542	26	38	of	of	ADP
ajst-32542	26	39	the	the	DET
ajst-32542	26	40	models	model	NOUN
ajst-32542	26	41	.	.	PUNCT
ajst-32542	27	1	therefore	therefore	ADV
ajst-32542	27	2	,	,	PUNCT
ajst-32542	27	3	how	how	SCONJ
ajst-32542	27	4	to	to	PART
ajst-32542	27	5	improve	improve	VERB
ajst-32542	27	6	the	the	DET
ajst-32542	27	7	model	model	NOUN
ajst-32542	27	8	's	's	PART
ajst-32542	27	9	interpretability	interpretability	NOUN
ajst-32542	27	10	and	and	CCONJ
ajst-32542	27	11	stability	stability	NOUN
ajst-32542	27	12	while	while	SCONJ
ajst-32542	27	13	ensuring	ensure	VERB
ajst-32542	27	14	prediction	prediction	NOUN
ajst-32542	27	15	accuracy	accuracy	NOUN
ajst-32542	27	16	remains	remain	VERB
ajst-32542	27	17	a	a	DET
ajst-32542	27	18	critical	critical	ADJ
ajst-32542	27	19	issue	issue	NOUN
ajst-32542	27	20	in	in	ADP
ajst-32542	27	21	the	the	DET
ajst-32542	27	22	field	field	NOUN
ajst-32542	27	23	of	of	ADP
ajst-32542	27	24	gold	gold	NOUN
ajst-32542	27	25	price	price	NOUN
ajst-32542	27	26	forecasting	forecasting	NOUN
ajst-32542	27	27	.	.	PUNCT
ajst-32542	28	1	2	2	X
ajst-32542	28	2	.	.	X
ajst-32542	28	3	model	model	NOUN
ajst-32542	28	4	methodology	methodology	PROPN
ajst-32542	28	5	2.1	2.1	NUM
ajst-32542	28	6	ceemdan	ceemdan	PROPN
ajst-32542	28	7	complete	complete	ADJ
ajst-32542	28	8	ensemble	ensemble	ADJ
ajst-32542	28	9	empirical	empirical	ADJ
ajst-32542	28	10	mode	mode	NOUN
ajst-32542	28	11	decomposition	decomposition	NOUN
ajst-32542	28	12	with	with	ADP
ajst-32542	28	13	adaptive	adaptive	ADJ
ajst-32542	28	14	noise	noise	NOUN
ajst-32542	28	15	(	(	PUNCT
ajst-32542	28	16	ceemdan	ceemdan	PROPN
ajst-32542	28	17	)	)	PUNCT
ajst-32542	28	18	is	be	AUX
ajst-32542	28	19	an	an	DET
ajst-32542	28	20	advanced	advanced	ADJ
ajst-32542	28	21	algorithm	algorithm	NOUN
ajst-32542	28	22	that	that	PRON
ajst-32542	28	23	further	far	ADV
ajst-32542	28	24	develops	develop	VERB
ajst-32542	28	25	empirical	empirical	ADJ
ajst-32542	28	26	mode	mode	NOUN
ajst-32542	28	27	decomposition	decomposition	NOUN
ajst-32542	28	28	(	(	PUNCT
ajst-32542	28	29	emd	emd	PROPN
ajst-32542	28	30	)	)	PUNCT
ajst-32542	28	31	and	and	CCONJ
ajst-32542	28	32	its	its	PRON
ajst-32542	28	33	improved	improved	ADJ
ajst-32542	28	34	version	version	NOUN
ajst-32542	28	35	,	,	PUNCT
ajst-32542	28	36	ensemble	ensemble	ADJ
ajst-32542	28	37	empirical	empirical	ADJ
ajst-32542	28	38	mode	mode	NOUN
ajst-32542	28	39	decomposition	decomposition	NOUN
ajst-32542	28	40	(	(	PUNCT
ajst-32542	28	41	eemd	eemd	PROPN
ajst-32542	28	42	)	)	PUNCT
ajst-32542	28	43	.	.	PUNCT
ajst-32542	29	1	traditional	traditional	PROPN
ajst-32542	29	2	emd	emd	PROPN
ajst-32542	29	3	decomposes	decompose	VERB
ajst-32542	29	4	nonstationary	nonstationary	ADJ
ajst-32542	29	5	signals	signal	NOUN
ajst-32542	29	6	into	into	ADP
ajst-32542	29	7	several	several	ADJ
ajst-32542	29	8	intrinsic	intrinsic	ADJ
ajst-32542	29	9	mode	mode	NOUN
ajst-32542	29	10	functions	function	NOUN
ajst-32542	29	11	(	(	PUNCT
ajst-32542	29	12	imfs	imfs	NOUN
ajst-32542	29	13	)	)	PUNCT
ajst-32542	29	14	and	and	CCONJ
ajst-32542	29	15	a	a	DET
ajst-32542	29	16	residue	residue	NOUN
ajst-32542	29	17	term	term	NOUN
ajst-32542	29	18	based	base	VERB
ajst-32542	29	19	on	on	ADP
ajst-32542	29	20	local	local	ADJ
ajst-32542	29	21	feature	feature	NOUN
ajst-32542	29	22	scales	scale	NOUN
ajst-32542	29	23	.	.	PUNCT
ajst-32542	30	1	however	however	ADV
ajst-32542	30	2	,	,	PUNCT
ajst-32542	30	3	emd	emd	PROPN
ajst-32542	30	4	is	be	AUX
ajst-32542	30	5	prone	prone	ADJ
ajst-32542	30	6	to	to	AUX
ajst-32542	30	7	mode	mode	VERB
ajst-32542	30	8	mixing	mix	VERB
ajst-32542	30	9	when	when	SCONJ
ajst-32542	30	10	processing	processing	NOUN
ajst-32542	30	11	signals	signal	NOUN
ajst-32542	30	12	with	with	ADP
ajst-32542	30	13	strong	strong	ADJ
ajst-32542	30	14	noise	noise	NOUN
ajst-32542	30	15	,	,	PUNCT
ajst-32542	30	16	leading	lead	VERB
ajst-32542	30	17	to	to	ADP
ajst-32542	30	18	the	the	DET
ajst-32542	30	19	blending	blending	NOUN
ajst-32542	30	20	of	of	ADP
ajst-32542	30	21	features	feature	NOUN
ajst-32542	30	22	from	from	ADP
ajst-32542	30	23	different	different	ADJ
ajst-32542	30	24	time	time	NOUN
ajst-32542	30	25	scales	scale	NOUN
ajst-32542	30	26	into	into	ADP
ajst-32542	30	27	the	the	DET
ajst-32542	30	28	same	same	ADJ
ajst-32542	30	29	imf	imf	NOUN
ajst-32542	30	30	,	,	PUNCT
ajst-32542	30	31	which	which	PRON
ajst-32542	30	32	affects	affect	VERB
ajst-32542	30	33	the	the	DET
ajst-32542	30	34	accuracy	accuracy	NOUN
ajst-32542	30	35	of	of	ADP
ajst-32542	30	36	signal	signal	ADJ
ajst-32542	30	37	feature	feature	NOUN
ajst-32542	30	38	extraction	extraction	NOUN
ajst-32542	30	39	.	.	PUNCT
ajst-32542	31	1	eemd	eemd	PROPN
ajst-32542	31	2	introduces	introduce	VERB
ajst-32542	31	3	white	white	ADJ
ajst-32542	31	4	noise	noise	NOUN
ajst-32542	31	5	and	and	CCONJ
ajst-32542	31	6	averages	average	VERB
ajst-32542	31	7	multiple	multiple	ADJ
ajst-32542	31	8	decompositions	decomposition	NOUN
ajst-32542	31	9	to	to	PART
ajst-32542	31	10	alleviate	alleviate	VERB
ajst-32542	31	11	the	the	DET
ajst-32542	31	12	mode	mode	NOUN
ajst-32542	31	13	mixing	mix	VERB
ajst-32542	31	14	problem	problem	NOUN
ajst-32542	31	15	,	,	PUNCT
ajst-32542	31	16	but	but	CCONJ
ajst-32542	31	17	it	it	PRON
ajst-32542	31	18	suffers	suffer	VERB
ajst-32542	31	19	from	from	ADP
ajst-32542	31	20	large	large	ADJ
ajst-32542	31	21	reconstruction	reconstruction	NOUN
ajst-32542	31	22	errors	error	NOUN
ajst-32542	31	23	and	and	CCONJ
ajst-32542	31	24	high	high	ADJ
ajst-32542	31	25	computational	computational	ADJ
ajst-32542	31	26	complexity	complexity	NOUN
ajst-32542	31	27	.	.	PUNCT
ajst-32542	32	1	ceemdan	ceemdan	PROPN
ajst-32542	32	2	improves	improve	VERB
ajst-32542	32	3	upon	upon	SCONJ
ajst-32542	32	4	eemd	eemd	NOUN
ajst-32542	32	5	by	by	ADP
ajst-32542	32	6	introducing	introduce	VERB
ajst-32542	32	7	an	an	DET
ajst-32542	32	8	adaptive	adaptive	ADJ
ajst-32542	32	9	noise	noise	NOUN
ajst-32542	32	10	strategy	strategy	NOUN
ajst-32542	32	11	,	,	PUNCT
ajst-32542	32	12	ensuring	ensure	VERB
ajst-32542	32	13	that	that	SCONJ
ajst-32542	32	14	the	the	DET
ajst-32542	32	15	imf	imf	PROPN
ajst-32542	32	16	components	component	NOUN
ajst-32542	32	17	obtained	obtain	VERB
ajst-32542	32	18	in	in	ADP
ajst-32542	32	19	each	each	DET
ajst-32542	32	20	decomposition	decomposition	NOUN
ajst-32542	32	21	step	step	NOUN
ajst-32542	32	22	more	more	ADV
ajst-32542	32	23	accurately	accurately	ADV
ajst-32542	32	24	represent	represent	VERB
ajst-32542	32	25	the	the	DET
ajst-32542	32	26	intrinsic	intrinsic	ADJ
ajst-32542	32	27	features	feature	NOUN
ajst-32542	32	28	of	of	ADP
ajst-32542	32	29	the	the	DET
ajst-32542	32	30	signal	signal	NOUN
ajst-32542	32	31	at	at	ADP
ajst-32542	32	32	corresponding	corresponding	ADJ
ajst-32542	32	33	time	time	NOUN
ajst-32542	32	34	scales	scale	NOUN
ajst-32542	32	35	.	.	PUNCT
ajst-32542	33	1	the	the	DET
ajst-32542	33	2	basic	basic	ADJ
ajst-32542	33	3	idea	idea	NOUN
ajst-32542	33	4	is	be	AUX
ajst-32542	33	5	to	to	PART
ajst-32542	33	6	add	add	VERB
ajst-32542	33	7	gaussian	gaussian	ADJ
ajst-32542	33	8	white	white	ADJ
ajst-32542	33	9	noise	noise	NOUN
ajst-32542	33	10	of	of	ADP
ajst-32542	33	11	varying	vary	VERB
ajst-32542	33	12	amplitude	amplitude	NOUN
ajst-32542	33	13	to	to	ADP
ajst-32542	33	14	the	the	DET
ajst-32542	33	15	original	original	ADJ
ajst-32542	33	16	signal	signal	NOUN
ajst-32542	33	17	and	and	CCONJ
ajst-32542	33	18	decompose	decompose	VERB
ajst-32542	33	19	it	it	PRON
ajst-32542	33	20	multiple	multiple	ADJ
ajst-32542	33	21	times	time	NOUN
ajst-32542	33	22	to	to	PART
ajst-32542	33	23	obtain	obtain	VERB
ajst-32542	33	24	the	the	DET
ajst-32542	33	25	first	first	ADJ
ajst-32542	33	26	-	-	PUNCT
ajst-32542	33	27	order	order	NOUN
ajst-32542	33	28	imf	imf	NOUN
ajst-32542	33	29	by	by	ADP
ajst-32542	33	30	averaging	average	VERB
ajst-32542	33	31	each	each	DET
ajst-32542	33	32	decomposition	decomposition	NOUN
ajst-32542	33	33	.	.	PUNCT
ajst-32542	34	1	the	the	DET
ajst-32542	34	2	imf	imf	PROPN
ajst-32542	34	3	is	be	AUX
ajst-32542	34	4	then	then	ADV
ajst-32542	34	5	removed	remove	VERB
ajst-32542	34	6	from	from	ADP
ajst-32542	34	7	the	the	DET
ajst-32542	34	8	original	original	ADJ
ajst-32542	34	9	signal	signal	NOUN
ajst-32542	34	10	,	,	PUNCT
ajst-32542	34	11	and	and	CCONJ
ajst-32542	34	12	adaptive	adaptive	ADJ
ajst-32542	34	13	noise	noise	NOUN
ajst-32542	34	14	is	be	AUX
ajst-32542	34	15	added	add	VERB
ajst-32542	34	16	to	to	ADP
ajst-32542	34	17	the	the	DET
ajst-32542	34	18	residual	residual	ADJ
ajst-32542	34	19	signal	signal	NOUN
ajst-32542	34	20	for	for	ADP
ajst-32542	34	21	further	further	ADJ
ajst-32542	34	22	decomposition	decomposition	NOUN
ajst-32542	34	23	.	.	PUNCT
ajst-32542	35	1	this	this	DET
ajst-32542	35	2	process	process	NOUN
ajst-32542	35	3	is	be	AUX
ajst-32542	35	4	repeated	repeat	VERB
ajst-32542	35	5	until	until	SCONJ
ajst-32542	35	6	all	all	DET
ajst-32542	35	7	imfs	imfs	NOUN
ajst-32542	35	8	are	be	AUX
ajst-32542	35	9	obtained	obtain	VERB
ajst-32542	35	10	.	.	PUNCT
ajst-32542	36	1	compared	compare	VERB
ajst-32542	36	2	to	to	ADP
ajst-32542	36	3	eemd	eemd	PROPN
ajst-32542	36	4	,	,	PUNCT
ajst-32542	36	5	ceemdan	ceemdan	PROPN
ajst-32542	36	6	offers	offer	VERB
ajst-32542	36	7	higher	high	ADJ
ajst-32542	36	8	signal	signal	NOUN
ajst-32542	36	9	reconstruction	reconstruction	NOUN
ajst-32542	36	10	accuracy	accuracy	NOUN
ajst-32542	36	11	and	and	CCONJ
ajst-32542	36	12	better	well	ADJ
ajst-32542	36	13	stability	stability	NOUN
ajst-32542	36	14	,	,	PUNCT
ajst-32542	36	15	effectively	effectively	ADV
ajst-32542	36	16	avoiding	avoid	VERB
ajst-32542	36	17	the	the	DET
ajst-32542	36	18	generation	generation	NOUN
ajst-32542	36	19	of	of	ADP
ajst-32542	36	20	pseudo	pseudo	NOUN
ajst-32542	36	21	-	-	NOUN
ajst-32542	36	22	imfs	imfs	NOUN
ajst-32542	36	23	while	while	SCONJ
ajst-32542	36	24	preserving	preserve	VERB
ajst-32542	36	25	the	the	DET
ajst-32542	36	26	nonlinear	nonlinear	ADJ
ajst-32542	36	27	and	and	CCONJ
ajst-32542	36	28	nonstationary	nonstationary	ADJ
ajst-32542	36	29	characteristics	characteristic	NOUN
ajst-32542	36	30	of	of	ADP
ajst-32542	36	31	the	the	DET
ajst-32542	36	32	signal	signal	NOUN
ajst-32542	36	33	.	.	PUNCT
ajst-32542	37	1	ceemdan	ceemdan	PROPN
ajst-32542	37	2	's	's	PART
ajst-32542	37	3	good	good	ADJ
ajst-32542	37	4	adaptability	adaptability	NOUN
ajst-32542	37	5	and	and	CCONJ
ajst-32542	37	6	high	high	ADJ
ajst-32542	37	7	-	-	PUNCT
ajst-32542	37	8	precision	precision	NOUN
ajst-32542	37	9	signal	signal	NOUN
ajst-32542	37	10	reconstruction	reconstruction	NOUN
ajst-32542	37	11	capabilities	capability	NOUN
ajst-32542	37	12	have	have	AUX
ajst-32542	37	13	made	make	VERB
ajst-32542	37	14	it	it	PRON
ajst-32542	37	15	widely	widely	ADV
ajst-32542	37	16	used	use	VERB
ajst-32542	37	17	in	in	ADP
ajst-32542	37	18	nonlinear	nonlinear	ADJ
ajst-32542	37	19	,	,	PUNCT
ajst-32542	37	20	non	non	ADJ
ajst-32542	37	21	-	-	ADJ
ajst-32542	37	22	stationary	stationary	ADJ
ajst-32542	37	23	signal	signal	NOUN
ajst-32542	37	24	analysis	analysis	NOUN
ajst-32542	37	25	fields	field	NOUN
ajst-32542	37	26	,	,	PUNCT
ajst-32542	37	27	such	such	ADJ
ajst-32542	37	28	as	as	ADP
ajst-32542	37	29	fault	fault	NOUN
ajst-32542	37	30	diagnosis	diagnosis	NOUN
ajst-32542	37	31	,	,	PUNCT
ajst-32542	37	32	seismic	seismic	ADJ
ajst-32542	37	33	wave	wave	NOUN
ajst-32542	37	34	analysis	analysis	NOUN
ajst-32542	37	35	,	,	PUNCT
ajst-32542	37	36	financial	financial	ADJ
ajst-32542	37	37	time	time	NOUN
ajst-32542	37	38	series	series	PROPN
ajst-32542	37	39	modeling	modeling	NOUN
ajst-32542	37	40	,	,	PUNCT
ajst-32542	37	41	and	and	CCONJ
ajst-32542	37	42	energy	energy	NOUN
ajst-32542	37	43	consumption	consumption	NOUN
ajst-32542	37	44	forecasting	forecasting	NOUN
ajst-32542	37	45	.	.	PUNCT
ajst-32542	38	1	in	in	ADP
ajst-32542	38	2	this	this	DET
ajst-32542	38	3	study	study	NOUN
ajst-32542	38	4	,	,	PUNCT
ajst-32542	38	5	ceemdan	ceemdan	PROPN
ajst-32542	38	6	is	be	AUX
ajst-32542	38	7	used	use	VERB
ajst-32542	38	8	to	to	PART
ajst-32542	38	9	decompose	decompose	VERB
ajst-32542	38	10	the	the	DET
ajst-32542	38	11	original	original	ADJ
ajst-32542	38	12	time	time	NOUN
ajst-32542	38	13	series	series	NOUN
ajst-32542	38	14	signal	signal	NOUN
ajst-32542	38	15	,	,	PUNCT
ajst-32542	38	16	extracting	extract	VERB
ajst-32542	38	17	imf	imf	PROPN
ajst-32542	38	18	components	component	NOUN
ajst-32542	38	19	at	at	ADP
ajst-32542	38	20	different	different	ADJ
ajst-32542	38	21	frequency	frequency	NOUN
ajst-32542	38	22	scales	scale	NOUN
ajst-32542	38	23	,	,	PUNCT
ajst-32542	38	24	providing	provide	VERB
ajst-32542	38	25	more	more	ADV
ajst-32542	38	26	physically	physically	ADV
ajst-32542	38	27	meaningful	meaningful	ADJ
ajst-32542	38	28	input	input	NOUN
ajst-32542	38	29	features	feature	NOUN
ajst-32542	38	30	for	for	ADP
ajst-32542	38	31	the	the	DET
ajst-32542	38	32	subsequent	subsequent	ADJ
ajst-32542	38	33	xgboost	xgboost	ADV
ajst-32542	38	34	-	-	PUNCT
ajst-32542	38	35	based	base	VERB
ajst-32542	38	36	prediction	prediction	NOUN
ajst-32542	38	37	model	model	NOUN
ajst-32542	38	38	and	and	CCONJ
ajst-32542	38	39	improving	improve	VERB
ajst-32542	38	40	overall	overall	ADJ
ajst-32542	38	41	prediction	prediction	NOUN
ajst-32542	38	42	performance	performance	NOUN
ajst-32542	38	43	and	and	CCONJ
ajst-32542	38	44	model	model	NOUN
ajst-32542	38	45	generalization	generalization	NOUN
ajst-32542	38	46	ability	ability	NOUN
ajst-32542	38	47	.	.	PUNCT
ajst-32542	39	1	2.2	2.2	NUM
ajst-32542	39	2	xgboost	xgboost	ADV
ajst-32542	39	3	extreme	extreme	ADJ
ajst-32542	39	4	gradient	gradient	NOUN
ajst-32542	39	5	boosting	boost	VERB
ajst-32542	39	6	(	(	PUNCT
ajst-32542	39	7	xgboost	xgboost	X
ajst-32542	39	8	)	)	PUNCT
ajst-32542	39	9	is	be	AUX
ajst-32542	39	10	an	an	DET
ajst-32542	39	11	efficient	efficient	ADJ
ajst-32542	39	12	ensemble	ensemble	ADJ
ajst-32542	39	13	learning	learning	NOUN
ajst-32542	39	14	algorithm	algorithm	NOUN
ajst-32542	39	15	based	base	VERB
ajst-32542	39	16	on	on	ADP
ajst-32542	39	17	the	the	DET
ajst-32542	39	18	gradient	gradient	NOUN
ajst-32542	39	19	boosting	boost	VERB
ajst-32542	39	20	framework	framework	NOUN
ajst-32542	39	21	.	.	PUNCT
ajst-32542	40	1	it	it	PRON
ajst-32542	40	2	makes	make	VERB
ajst-32542	40	3	several	several	ADJ
ajst-32542	40	4	improvements	improvement	NOUN
ajst-32542	40	5	over	over	ADP
ajst-32542	40	6	the	the	DET
ajst-32542	40	7	traditional	traditional	ADJ
ajst-32542	40	8	gradient	gradient	NOUN
ajst-32542	40	9	boosting	boost	VERB
ajst-32542	40	10	decision	decision	NOUN
ajst-32542	40	11	tree	tree	NOUN
ajst-32542	40	12	(	(	PUNCT
ajst-32542	40	13	gbdt	gbdt	PROPN
ajst-32542	40	14	)	)	PUNCT
ajst-32542	40	15	,	,	PUNCT
ajst-32542	40	16	offering	offer	VERB
ajst-32542	40	17	higher	high	ADJ
ajst-32542	40	18	computational	computational	ADJ
ajst-32542	40	19	efficiency	efficiency	NOUN
ajst-32542	40	20	,	,	PUNCT
ajst-32542	40	21	stronger	strong	ADJ
ajst-32542	40	22	generalization	generalization	NOUN
ajst-32542	40	23	ability	ability	NOUN
ajst-32542	40	24	,	,	PUNCT
ajst-32542	40	25	and	and	CCONJ
ajst-32542	40	26	excellent	excellent	ADJ
ajst-32542	40	27	model	model	NOUN
ajst-32542	40	28	stability	stability	NOUN
ajst-32542	40	29	.	.	PUNCT
ajst-32542	41	1	xgboost	xgboost	PROPN
ajst-32542	41	2	builds	build	VERB
ajst-32542	41	3	multiple	multiple	ADJ
ajst-32542	41	4	weak	weak	ADJ
ajst-32542	41	5	learners	learner	NOUN
ajst-32542	41	6	(	(	PUNCT
ajst-32542	41	7	usually	usually	ADV
ajst-32542	41	8	regression	regression	VERB
ajst-32542	41	9	trees	tree	NOUN
ajst-32542	41	10	)	)	PUNCT
ajst-32542	41	11	and	and	CCONJ
ajst-32542	41	12	90	90	NUM
ajst-32542	41	13	iteratively	iteratively	ADV
ajst-32542	41	14	optimizes	optimize	VERB
ajst-32542	41	15	the	the	DET
ajst-32542	41	16	loss	loss	NOUN
ajst-32542	41	17	function	function	NOUN
ajst-32542	41	18	in	in	ADP
ajst-32542	41	19	an	an	DET
ajst-32542	41	20	additive	additive	ADJ
ajst-32542	41	21	model	model	NOUN
ajst-32542	41	22	form	form	NOUN
ajst-32542	41	23	to	to	PART
ajst-32542	41	24	achieve	achieve	VERB
ajst-32542	41	25	high	high	ADJ
ajst-32542	41	26	-	-	PUNCT
ajst-32542	41	27	precision	precision	NOUN
ajst-32542	41	28	fitting	fitting	NOUN
ajst-32542	41	29	of	of	ADP
ajst-32542	41	30	nonlinear	nonlinear	ADJ
ajst-32542	41	31	relationships	relationship	NOUN
ajst-32542	41	32	.	.	PUNCT
ajst-32542	42	1	compared	compare	VERB
ajst-32542	42	2	to	to	ADP
ajst-32542	42	3	traditional	traditional	ADJ
ajst-32542	42	4	gbdt	gbdt	NOUN
ajst-32542	42	5	,	,	PUNCT
ajst-32542	42	6	the	the	DET
ajst-32542	42	7	main	main	ADJ
ajst-32542	42	8	improvements	improvement	NOUN
ajst-32542	42	9	of	of	ADP
ajst-32542	42	10	xgboost	xgboost	PRON
ajst-32542	42	11	include	include	VERB
ajst-32542	42	12	:	:	PUNCT
ajst-32542	42	13	first	first	ADV
ajst-32542	42	14	,	,	PUNCT
ajst-32542	42	15	the	the	DET
ajst-32542	42	16	introduction	introduction	NOUN
ajst-32542	42	17	of	of	ADP
ajst-32542	42	18	second	second	ADJ
ajst-32542	42	19	-	-	PUNCT
ajst-32542	42	20	order	order	NOUN
ajst-32542	42	21	derivative	derivative	ADJ
ajst-32542	42	22	information	information	NOUN
ajst-32542	42	23	to	to	PART
ajst-32542	42	24	accelerate	accelerate	VERB
ajst-32542	42	25	convergence	convergence	NOUN
ajst-32542	42	26	and	and	CCONJ
ajst-32542	42	27	make	make	VERB
ajst-32542	42	28	the	the	DET
ajst-32542	42	29	approximation	approximation	NOUN
ajst-32542	42	30	of	of	ADP
ajst-32542	42	31	the	the	DET
ajst-32542	42	32	loss	loss	NOUN
ajst-32542	42	33	function	function	VERB
ajst-32542	42	34	more	more	ADV
ajst-32542	42	35	accurate	accurate	ADJ
ajst-32542	42	36	;	;	PUNCT
ajst-32542	42	37	second	second	ADJ
ajst-32542	42	38	,	,	PUNCT
ajst-32542	42	39	the	the	DET
ajst-32542	42	40	addition	addition	NOUN
ajst-32542	42	41	of	of	ADP
ajst-32542	42	42	a	a	DET
ajst-32542	42	43	regularization	regularization	NOUN
ajst-32542	42	44	term	term	NOUN
ajst-32542	42	45	to	to	ADP
ajst-32542	42	46	the	the	DET
ajst-32542	42	47	objective	objective	ADJ
ajst-32542	42	48	function	function	NOUN
ajst-32542	42	49	,	,	PUNCT
ajst-32542	42	50	effectively	effectively	ADV
ajst-32542	42	51	controlling	control	VERB
ajst-32542	42	52	model	model	NOUN
ajst-32542	42	53	complexity	complexity	NOUN
ajst-32542	42	54	and	and	CCONJ
ajst-32542	42	55	preventing	prevent	VERB
ajst-32542	42	56	overfitting	overfitting	NOUN
ajst-32542	42	57	;	;	PUNCT
ajst-32542	42	58	furthermore	furthermore	ADV
ajst-32542	42	59	,	,	PUNCT
ajst-32542	42	60	xgboost	xgboost	ADV
ajst-32542	42	61	employs	employ	VERB
ajst-32542	42	62	feature	feature	NOUN
ajst-32542	42	63	selection	selection	NOUN
ajst-32542	42	64	strategies	strategy	NOUN
ajst-32542	42	65	based	base	VERB
ajst-32542	42	66	on	on	ADP
ajst-32542	42	67	approximation	approximation	NOUN
ajst-32542	42	68	algorithms	algorithm	NOUN
ajst-32542	42	69	when	when	SCONJ
ajst-32542	42	70	splitting	splitting	NOUN
ajst-32542	42	71	nodes	node	NOUN
ajst-32542	42	72	and	and	CCONJ
ajst-32542	42	73	supports	support	VERB
ajst-32542	42	74	sparse	sparse	ADJ
ajst-32542	42	75	matrix	matrix	NOUN
ajst-32542	42	76	processing	processing	NOUN
ajst-32542	42	77	and	and	CCONJ
ajst-32542	42	78	parallel	parallel	ADJ
ajst-32542	42	79	computing	computing	NOUN
ajst-32542	42	80	,	,	PUNCT
ajst-32542	42	81	greatly	greatly	ADV
ajst-32542	42	82	enhancing	enhance	VERB
ajst-32542	42	83	training	training	NOUN
ajst-32542	42	84	efficiency	efficiency	NOUN
ajst-32542	42	85	and	and	CCONJ
ajst-32542	42	86	model	model	NOUN
ajst-32542	42	87	scalability	scalability	NOUN
ajst-32542	42	88	.	.	PUNCT
ajst-32542	43	1	the	the	DET
ajst-32542	43	2	objective	objective	ADJ
ajst-32542	43	3	function	function	NOUN
ajst-32542	43	4	of	of	ADP
ajst-32542	43	5	xgboost	xgboost	PROPN
ajst-32542	43	6	can	can	AUX
ajst-32542	43	7	be	be	AUX
ajst-32542	43	8	expressed	express	VERB
ajst-32542	43	9	as	as	ADP
ajst-32542	43	10	:	:	PUNCT
ajst-32542	43	11	(	(	PUNCT
ajst-32542	43	12	1	1	X
ajst-32542	43	13	)	)	PUNCT
ajst-32542	43	14	in	in	ADP
ajst-32542	43	15	time	time	NOUN
ajst-32542	43	16	series	series	PROPN
ajst-32542	43	17	modeling	modeling	NOUN
ajst-32542	43	18	and	and	CCONJ
ajst-32542	43	19	prediction	prediction	NOUN
ajst-32542	43	20	tasks	task	NOUN
ajst-32542	43	21	,	,	PUNCT
ajst-32542	43	22	xgboost	xgboost	ADV
ajst-32542	43	23	demonstrates	demonstrate	VERB
ajst-32542	43	24	excellent	excellent	ADJ
ajst-32542	43	25	prediction	prediction	NOUN
ajst-32542	43	26	performance	performance	NOUN
ajst-32542	43	27	with	with	ADP
ajst-32542	43	28	its	its	PRON
ajst-32542	43	29	powerful	powerful	ADJ
ajst-32542	43	30	nonlinear	nonlinear	ADJ
ajst-32542	43	31	modeling	modeling	NOUN
ajst-32542	43	32	capabilities	capability	NOUN
ajst-32542	43	33	and	and	CCONJ
ajst-32542	43	34	adaptability	adaptability	NOUN
ajst-32542	43	35	to	to	ADP
ajst-32542	43	36	high	high	ADJ
ajst-32542	43	37	-	-	PUNCT
ajst-32542	43	38	dimensional	dimensional	ADJ
ajst-32542	43	39	features	feature	NOUN
ajst-32542	43	40	.	.	PUNCT
ajst-32542	44	1	unlike	unlike	ADP
ajst-32542	44	2	traditional	traditional	ADJ
ajst-32542	44	3	statistical	statistical	ADJ
ajst-32542	44	4	models	model	NOUN
ajst-32542	44	5	(	(	PUNCT
ajst-32542	44	6	e.g.	e.g.	ADV
ajst-32542	44	7	,	,	PUNCT
ajst-32542	44	8	arima	arima	PROPN
ajst-32542	44	9	,	,	PUNCT
ajst-32542	44	10	svr	svr	PROPN
ajst-32542	44	11	)	)	PUNCT
ajst-32542	44	12	,	,	PUNCT
ajst-32542	44	13	xgboost	xgboost	PROPN
ajst-32542	44	14	does	do	AUX
ajst-32542	44	15	not	not	PART
ajst-32542	44	16	rely	rely	VERB
ajst-32542	44	17	on	on	ADP
ajst-32542	44	18	data	datum	NOUN
ajst-32542	44	19	stationarity	stationarity	NOUN
ajst-32542	44	20	assumptions	assumption	NOUN
ajst-32542	44	21	,	,	PUNCT
ajst-32542	44	22	making	make	VERB
ajst-32542	44	23	it	it	PRON
ajst-32542	44	24	capable	capable	ADJ
ajst-32542	44	25	of	of	ADP
ajst-32542	44	26	flexibly	flexibly	ADV
ajst-32542	44	27	capturing	capture	VERB
ajst-32542	44	28	complex	complex	ADJ
ajst-32542	44	29	time	time	NOUN
ajst-32542	44	30	series	series	NOUN
ajst-32542	44	31	features	feature	NOUN
ajst-32542	44	32	and	and	CCONJ
ajst-32542	44	33	multi	multi	ADJ
ajst-32542	44	34	-	-	ADJ
ajst-32542	44	35	scale	scale	ADJ
ajst-32542	44	36	dynamic	dynamic	ADJ
ajst-32542	44	37	changes	change	NOUN
ajst-32542	44	38	.	.	PUNCT
ajst-32542	45	1	by	by	ADP
ajst-32542	45	2	weighting	weight	VERB
ajst-32542	45	3	and	and	CCONJ
ajst-32542	45	4	combining	combine	VERB
ajst-32542	45	5	input	input	NOUN
ajst-32542	45	6	features	feature	NOUN
ajst-32542	45	7	,	,	PUNCT
ajst-32542	45	8	xgboost	xgboost	X
ajst-32542	45	9	effectively	effectively	ADV
ajst-32542	45	10	explores	explore	VERB
ajst-32542	45	11	potential	potential	ADJ
ajst-32542	45	12	variable	variable	ADJ
ajst-32542	45	13	interaction	interaction	NOUN
ajst-32542	45	14	relationships	relationship	NOUN
ajst-32542	45	15	,	,	PUNCT
ajst-32542	45	16	thus	thus	ADV
ajst-32542	45	17	enabling	enable	VERB
ajst-32542	45	18	precise	precise	ADJ
ajst-32542	45	19	prediction	prediction	NOUN
ajst-32542	45	20	of	of	ADP
ajst-32542	45	21	the	the	DET
ajst-32542	45	22	target	target	NOUN
ajst-32542	45	23	variable	variable	NOUN
ajst-32542	45	24	.	.	PUNCT
ajst-32542	46	1	in	in	ADP
ajst-32542	46	2	this	this	DET
ajst-32542	46	3	study	study	NOUN
ajst-32542	46	4	,	,	PUNCT
ajst-32542	46	5	xgboost	xgboost	ADV
ajst-32542	46	6	is	be	AUX
ajst-32542	46	7	used	use	VERB
ajst-32542	46	8	to	to	PART
ajst-32542	46	9	model	model	VERB
ajst-32542	46	10	and	and	CCONJ
ajst-32542	46	11	predict	predict	VERB
ajst-32542	46	12	the	the	DET
ajst-32542	46	13	imfs	imfs	NOUN
ajst-32542	46	14	and	and	CCONJ
ajst-32542	46	15	residual	residual	ADJ
ajst-32542	46	16	signals	signal	NOUN
ajst-32542	46	17	obtained	obtain	VERB
ajst-32542	46	18	from	from	ADP
ajst-32542	46	19	the	the	DET
ajst-32542	46	20	ceemdan	ceemdan	ADJ
ajst-32542	46	21	decomposition	decomposition	NOUN
ajst-32542	46	22	.	.	PUNCT
ajst-32542	47	1	each	each	DET
ajst-32542	47	2	imf	imf	PROPN
ajst-32542	47	3	component	component	NOUN
ajst-32542	47	4	corresponds	correspond	VERB
ajst-32542	47	5	to	to	ADP
ajst-32542	47	6	features	feature	NOUN
ajst-32542	47	7	at	at	ADP
ajst-32542	47	8	different	different	ADJ
ajst-32542	47	9	frequency	frequency	NOUN
ajst-32542	47	10	scales	scale	NOUN
ajst-32542	47	11	,	,	PUNCT
ajst-32542	47	12	and	and	CCONJ
ajst-32542	47	13	xgboost	xgboost	X
ajst-32542	47	14	can	can	AUX
ajst-32542	47	15	independently	independently	ADV
ajst-32542	47	16	train	train	VERB
ajst-32542	47	17	sub	sub	NOUN
ajst-32542	47	18	-	-	NOUN
ajst-32542	47	19	models	model	NOUN
ajst-32542	47	20	for	for	ADP
ajst-32542	47	21	the	the	DET
ajst-32542	47	22	temporal	temporal	ADJ
ajst-32542	47	23	characteristics	characteristic	NOUN
ajst-32542	47	24	of	of	ADP
ajst-32542	47	25	each	each	DET
ajst-32542	47	26	component	component	NOUN
ajst-32542	47	27	,	,	PUNCT
ajst-32542	47	28	achieving	achieve	VERB
ajst-32542	47	29	fusion	fusion	NOUN
ajst-32542	47	30	of	of	ADP
ajst-32542	47	31	multi	multi	ADJ
ajst-32542	47	32	-	-	ADJ
ajst-32542	47	33	scale	scale	ADJ
ajst-32542	47	34	information	information	NOUN
ajst-32542	47	35	.	.	PUNCT
ajst-32542	48	1	finally	finally	ADV
ajst-32542	48	2	,	,	PUNCT
ajst-32542	48	3	the	the	DET
ajst-32542	48	4	results	result	NOUN
ajst-32542	48	5	of	of	ADP
ajst-32542	48	6	each	each	DET
ajst-32542	48	7	sub	sub	NOUN
ajst-32542	48	8	-	-	NOUN
ajst-32542	48	9	model	model	NOUN
ajst-32542	48	10	are	be	AUX
ajst-32542	48	11	weighted	weight	VERB
ajst-32542	48	12	and	and	CCONJ
ajst-32542	48	13	reconstructed	reconstruct	VERB
ajst-32542	48	14	to	to	PART
ajst-32542	48	15	effectively	effectively	ADV
ajst-32542	48	16	improve	improve	VERB
ajst-32542	48	17	overall	overall	ADJ
ajst-32542	48	18	prediction	prediction	NOUN
ajst-32542	48	19	accuracy	accuracy	NOUN
ajst-32542	48	20	and	and	CCONJ
ajst-32542	48	21	robustness	robustness	NOUN
ajst-32542	48	22	.	.	PUNCT
ajst-32542	49	1	this	this	DET
ajst-32542	49	2	approach	approach	NOUN
ajst-32542	49	3	fully	fully	ADV
ajst-32542	49	4	combines	combine	VERB
ajst-32542	49	5	ceemdan	ceemdan	PROPN
ajst-32542	49	6	's	's	PART
ajst-32542	49	7	signal	signal	ADJ
ajst-32542	49	8	decomposition	decomposition	NOUN
ajst-32542	49	9	ability	ability	NOUN
ajst-32542	49	10	with	with	ADP
ajst-32542	49	11	xgboost	xgboost	PROPN
ajst-32542	49	12	's	's	PART
ajst-32542	49	13	nonlinear	nonlinear	ADJ
ajst-32542	49	14	modeling	modeling	NOUN
ajst-32542	49	15	advantages	advantage	NOUN
ajst-32542	49	16	,	,	PUNCT
ajst-32542	49	17	providing	provide	VERB
ajst-32542	49	18	an	an	DET
ajst-32542	49	19	efficient	efficient	ADJ
ajst-32542	49	20	and	and	CCONJ
ajst-32542	49	21	reliable	reliable	ADJ
ajst-32542	49	22	hybrid	hybrid	ADJ
ajst-32542	49	23	modeling	modeling	NOUN
ajst-32542	49	24	framework	framework	NOUN
ajst-32542	49	25	for	for	ADP
ajst-32542	49	26	predicting	predict	VERB
ajst-32542	49	27	complex	complex	ADJ
ajst-32542	49	28	non	non	ADJ
ajst-32542	49	29	-	-	ADJ
ajst-32542	49	30	stationary	stationary	ADJ
ajst-32542	49	31	signals	signal	NOUN
ajst-32542	49	32	.	.	PUNCT
ajst-32542	50	1	3	3	X
ajst-32542	50	2	.	.	X
ajst-32542	50	3	dataset	dataset	NOUN
ajst-32542	50	4	overview	overview	VERB
ajst-32542	50	5	3.1	3.1	NUM
ajst-32542	50	6	data	datum	NOUN
ajst-32542	50	7	partitioning	partitioning	NOUN
ajst-32542	50	8	and	and	CCONJ
ajst-32542	50	9	visualization	visualization	NOUN
ajst-32542	50	10	figure	figure	NOUN
ajst-32542	50	11	1	1	NUM
ajst-32542	50	12	.	.	NOUN
ajst-32542	50	13	time	time	NOUN
ajst-32542	50	14	series	series	NOUN
ajst-32542	50	15	trend	trend	NOUN
ajst-32542	50	16	of	of	ADP
ajst-32542	50	17	gold	gold	NOUN
ajst-32542	50	18	prices	price	NOUN
ajst-32542	50	19	91	91	NUM
ajst-32542	50	20	this	this	DET
ajst-32542	50	21	study	study	NOUN
ajst-32542	50	22	uses	use	VERB
ajst-32542	50	23	historical	historical	ADJ
ajst-32542	50	24	gold	gold	NOUN
ajst-32542	50	25	price	price	NOUN
ajst-32542	50	26	data	datum	NOUN
ajst-32542	50	27	as	as	ADP
ajst-32542	50	28	the	the	DET
ajst-32542	50	29	experimental	experimental	ADJ
ajst-32542	50	30	dataset	dataset	NOUN
ajst-32542	50	31	,	,	PUNCT
ajst-32542	50	32	sourced	source	VERB
ajst-32542	50	33	from	from	ADP
ajst-32542	50	34	publicly	publicly	ADV
ajst-32542	50	35	available	available	ADJ
ajst-32542	50	36	financial	financial	ADJ
ajst-32542	50	37	market	market	NOUN
ajst-32542	50	38	data	datum	NOUN
ajst-32542	50	39	.	.	PUNCT
ajst-32542	51	1	the	the	DET
ajst-32542	51	2	time	time	NOUN
ajst-32542	51	3	span	span	NOUN
ajst-32542	51	4	covers	cover	VERB
ajst-32542	51	5	from	from	ADP
ajst-32542	51	6	october	october	PROPN
ajst-32542	51	7	31	31	NUM
ajst-32542	51	8	,	,	PUNCT
ajst-32542	51	9	2012	2012	NUM
ajst-32542	51	10	,	,	PUNCT
ajst-32542	51	11	to	to	ADP
ajst-32542	51	12	october	october	PROPN
ajst-32542	51	13	28	28	NUM
ajst-32542	51	14	,	,	PUNCT
ajst-32542	51	15	2022	2022	NUM
ajst-32542	51	16	,	,	PUNCT
ajst-32542	51	17	and	and	CCONJ
ajst-32542	51	18	includes	include	VERB
ajst-32542	51	19	daily	daily	ADJ
ajst-32542	51	20	opening	opening	NOUN
ajst-32542	51	21	prices	price	NOUN
ajst-32542	51	22	of	of	ADP
ajst-32542	51	23	gold	gold	NOUN
ajst-32542	51	24	during	during	ADP
ajst-32542	51	25	this	this	DET
ajst-32542	51	26	period	period	NOUN
ajst-32542	51	27	.	.	PUNCT
ajst-32542	52	1	to	to	PART
ajst-32542	52	2	construct	construct	VERB
ajst-32542	52	3	effective	effective	ADJ
ajst-32542	52	4	training	training	NOUN
ajst-32542	52	5	and	and	CCONJ
ajst-32542	52	6	testing	testing	NOUN
ajst-32542	52	7	sets	set	NOUN
ajst-32542	52	8	,	,	PUNCT
ajst-32542	52	9	the	the	DET
ajst-32542	52	10	dataset	dataset	NOUN
ajst-32542	52	11	is	be	AUX
ajst-32542	52	12	partitioned	partition	VERB
ajst-32542	52	13	in	in	ADP
ajst-32542	52	14	an	an	DET
ajst-32542	52	15	8:1:1	8:1:1	NUM
ajst-32542	52	16	ratio	ratio	NOUN
ajst-32542	52	17	,	,	PUNCT
ajst-32542	52	18	with	with	ADP
ajst-32542	52	19	80	80	NUM
ajst-32542	52	20	%	%	NOUN
ajst-32542	52	21	of	of	ADP
ajst-32542	52	22	the	the	DET
ajst-32542	52	23	data	datum	NOUN
ajst-32542	52	24	used	use	VERB
ajst-32542	52	25	for	for	ADP
ajst-32542	52	26	model	model	NOUN
ajst-32542	52	27	training	training	NOUN
ajst-32542	52	28	,	,	PUNCT
ajst-32542	52	29	10	10	NUM
ajst-32542	52	30	%	%	NOUN
ajst-32542	52	31	for	for	ADP
ajst-32542	52	32	model	model	NOUN
ajst-32542	52	33	validation	validation	NOUN
ajst-32542	52	34	,	,	PUNCT
ajst-32542	52	35	and	and	CCONJ
ajst-32542	52	36	the	the	DET
ajst-32542	52	37	remaining	remain	VERB
ajst-32542	52	38	10	10	NUM
ajst-32542	52	39	%	%	NOUN
ajst-32542	52	40	for	for	ADP
ajst-32542	52	41	final	final	ADJ
ajst-32542	52	42	testing	testing	NOUN
ajst-32542	52	43	.	.	PUNCT
ajst-32542	53	1	this	this	DET
ajst-32542	53	2	partitioning	partition	VERB
ajst-32542	53	3	ratio	ratio	NOUN
ajst-32542	53	4	ensures	ensure	VERB
ajst-32542	53	5	that	that	SCONJ
ajst-32542	53	6	the	the	DET
ajst-32542	53	7	model	model	NOUN
ajst-32542	53	8	can	can	AUX
ajst-32542	53	9	fully	fully	ADV
ajst-32542	53	10	learn	learn	VERB
ajst-32542	53	11	the	the	DET
ajst-32542	53	12	trends	trend	NOUN
ajst-32542	53	13	and	and	CCONJ
ajst-32542	53	14	patterns	pattern	NOUN
ajst-32542	53	15	in	in	ADP
ajst-32542	53	16	the	the	DET
ajst-32542	53	17	data	datum	NOUN
ajst-32542	53	18	while	while	SCONJ
ajst-32542	53	19	effectively	effectively	ADV
ajst-32542	53	20	evaluating	evaluate	VERB
ajst-32542	53	21	the	the	DET
ajst-32542	53	22	model	model	NOUN
ajst-32542	53	23	's	's	PART
ajst-32542	53	24	generalization	generalization	NOUN
ajst-32542	53	25	ability	ability	NOUN
ajst-32542	53	26	.	.	PUNCT
ajst-32542	54	1	to	to	PART
ajst-32542	54	2	better	well	ADV
ajst-32542	54	3	understand	understand	VERB
ajst-32542	54	4	the	the	DET
ajst-32542	54	5	distribution	distribution	NOUN
ajst-32542	54	6	characteristics	characteristic	NOUN
ajst-32542	54	7	of	of	ADP
ajst-32542	54	8	the	the	DET
ajst-32542	54	9	data	datum	NOUN
ajst-32542	54	10	,	,	PUNCT
ajst-32542	54	11	this	this	DET
ajst-32542	54	12	study	study	NOUN
ajst-32542	54	13	first	first	ADV
ajst-32542	54	14	conducts	conduct	VERB
ajst-32542	54	15	a	a	DET
ajst-32542	54	16	visualization	visualization	NOUN
ajst-32542	54	17	analysis	analysis	NOUN
ajst-32542	54	18	.	.	PUNCT
ajst-32542	55	1	by	by	ADP
ajst-32542	55	2	plotting	plot	VERB
ajst-32542	55	3	a	a	DET
ajst-32542	55	4	time	time	NOUN
ajst-32542	55	5	series	series	NOUN
ajst-32542	55	6	graph	graph	NOUN
ajst-32542	55	7	,	,	PUNCT
ajst-32542	55	8	figure	figure	NOUN
ajst-32542	55	9	1	1	NUM
ajst-32542	55	10	shows	show	VERB
ajst-32542	55	11	the	the	DET
ajst-32542	55	12	fluctuation	fluctuation	NOUN
ajst-32542	55	13	trend	trend	NOUN
ajst-32542	55	14	and	and	CCONJ
ajst-32542	55	15	periodic	periodic	ADJ
ajst-32542	55	16	variations	variation	NOUN
ajst-32542	55	17	of	of	ADP
ajst-32542	55	18	gold	gold	NOUN
ajst-32542	55	19	prices	price	NOUN
ajst-32542	55	20	over	over	ADP
ajst-32542	55	21	the	the	DET
ajst-32542	55	22	entire	entire	ADJ
ajst-32542	55	23	time	time	NOUN
ajst-32542	55	24	period	period	NOUN
ajst-32542	55	25	.	.	PUNCT
ajst-32542	56	1	from	from	ADP
ajst-32542	56	2	the	the	DET
ajst-32542	56	3	visual	visual	ADJ
ajst-32542	56	4	results	result	NOUN
ajst-32542	56	5	,	,	PUNCT
ajst-32542	56	6	it	it	PRON
ajst-32542	56	7	is	be	AUX
ajst-32542	56	8	evident	evident	ADJ
ajst-32542	56	9	that	that	SCONJ
ajst-32542	56	10	gold	gold	NOUN
ajst-32542	56	11	prices	price	NOUN
ajst-32542	56	12	exhibit	exhibit	VERB
ajst-32542	56	13	significant	significant	ADJ
ajst-32542	56	14	nonlinear	nonlinear	ADJ
ajst-32542	56	15	and	and	CCONJ
ajst-32542	56	16	non	non	ADJ
ajst-32542	56	17	-	-	ADJ
ajst-32542	56	18	stationary	stationary	ADJ
ajst-32542	56	19	characteristics	characteristic	NOUN
ajst-32542	56	20	at	at	ADP
ajst-32542	56	21	different	different	ADJ
ajst-32542	56	22	time	time	NOUN
ajst-32542	56	23	periods	period	NOUN
ajst-32542	56	24	,	,	PUNCT
ajst-32542	56	25	which	which	PRON
ajst-32542	56	26	provides	provide	VERB
ajst-32542	56	27	the	the	DET
ajst-32542	56	28	theoretical	theoretical	ADJ
ajst-32542	56	29	basis	basis	NOUN
ajst-32542	56	30	for	for	ADP
ajst-32542	56	31	applying	apply	VERB
ajst-32542	56	32	the	the	DET
ajst-32542	56	33	ceemdan	ceemdan	ADJ
ajst-32542	56	34	algorithm	algorithm	NOUN
ajst-32542	56	35	for	for	ADP
ajst-32542	56	36	signal	signal	ADJ
ajst-32542	56	37	decomposition	decomposition	NOUN
ajst-32542	56	38	in	in	ADP
ajst-32542	56	39	subsequent	subsequent	ADJ
ajst-32542	56	40	steps	step	NOUN
ajst-32542	56	41	.	.	PUNCT
ajst-32542	57	1	3.2	3.2	NUM
ajst-32542	57	2	experimental	experimental	ADJ
ajst-32542	57	3	framework	framework	NOUN
ajst-32542	57	4	the	the	DET
ajst-32542	57	5	experimental	experimental	ADJ
ajst-32542	57	6	framework	framework	NOUN
ajst-32542	57	7	of	of	ADP
ajst-32542	57	8	this	this	DET
ajst-32542	57	9	study	study	NOUN
ajst-32542	57	10	consists	consist	VERB
ajst-32542	57	11	of	of	ADP
ajst-32542	57	12	two	two	NUM
ajst-32542	57	13	main	main	ADJ
ajst-32542	57	14	steps	step	NOUN
ajst-32542	57	15	:	:	PUNCT
ajst-32542	57	16	first	first	ADV
ajst-32542	57	17	,	,	PUNCT
ajst-32542	57	18	ceemdan	ceemdan	PROPN
ajst-32542	57	19	is	be	AUX
ajst-32542	57	20	used	use	VERB
ajst-32542	57	21	to	to	PART
ajst-32542	57	22	preprocess	preprocess	VERB
ajst-32542	57	23	the	the	DET
ajst-32542	57	24	original	original	ADJ
ajst-32542	57	25	gold	gold	NOUN
ajst-32542	57	26	price	price	NOUN
ajst-32542	57	27	data	datum	NOUN
ajst-32542	57	28	to	to	PART
ajst-32542	57	29	extract	extract	VERB
ajst-32542	57	30	intrinsic	intrinsic	ADJ
ajst-32542	57	31	mode	mode	NOUN
ajst-32542	57	32	functions	function	NOUN
ajst-32542	57	33	(	(	PUNCT
ajst-32542	57	34	imfs	imfs	NOUN
ajst-32542	57	35	)	)	PUNCT
ajst-32542	57	36	at	at	ADP
ajst-32542	57	37	different	different	ADJ
ajst-32542	57	38	frequency	frequency	NOUN
ajst-32542	57	39	scales	scale	NOUN
ajst-32542	57	40	.	.	PUNCT
ajst-32542	58	1	second	second	ADJ
ajst-32542	58	2	,	,	PUNCT
ajst-32542	58	3	these	these	DET
ajst-32542	58	4	imf	imf	PROPN
ajst-32542	58	5	components	component	NOUN
ajst-32542	58	6	are	be	AUX
ajst-32542	58	7	used	use	VERB
ajst-32542	58	8	as	as	ADP
ajst-32542	58	9	input	input	NOUN
ajst-32542	58	10	features	feature	NOUN
ajst-32542	58	11	for	for	ADP
ajst-32542	58	12	prediction	prediction	NOUN
ajst-32542	58	13	with	with	ADP
ajst-32542	58	14	the	the	DET
ajst-32542	58	15	xgboost	xgboost	PROPN
ajst-32542	58	16	model	model	PROPN
ajst-32542	58	17	.	.	PUNCT
ajst-32542	59	1	the	the	DET
ajst-32542	59	2	framework	framework	NOUN
ajst-32542	59	3	aims	aim	VERB
ajst-32542	59	4	to	to	PART
ajst-32542	59	5	validate	validate	VERB
ajst-32542	59	6	the	the	DET
ajst-32542	59	7	effectiveness	effectiveness	NOUN
ajst-32542	59	8	of	of	ADP
ajst-32542	59	9	ceemdan	ceemdan	PROPN
ajst-32542	59	10	in	in	ADP
ajst-32542	59	11	extracting	extract	VERB
ajst-32542	59	12	signal	signal	NOUN
ajst-32542	59	13	features	feature	NOUN
ajst-32542	59	14	and	and	CCONJ
ajst-32542	59	15	assess	assess	VERB
ajst-32542	59	16	the	the	DET
ajst-32542	59	17	enhancement	enhancement	NOUN
ajst-32542	59	18	effect	effect	NOUN
ajst-32542	59	19	of	of	ADP
ajst-32542	59	20	combining	combine	VERB
ajst-32542	59	21	it	it	PRON
ajst-32542	59	22	with	with	ADP
ajst-32542	59	23	xgboost	xgboost	ADV
ajst-32542	59	24	for	for	ADP
ajst-32542	59	25	gold	gold	NOUN
ajst-32542	59	26	price	price	NOUN
ajst-32542	59	27	prediction	prediction	NOUN
ajst-32542	59	28	.	.	PUNCT
ajst-32542	60	1	specifically	specifically	ADV
ajst-32542	60	2	,	,	PUNCT
ajst-32542	60	3	the	the	DET
ajst-32542	60	4	ceemdan	ceemdan	PROPN
ajst-32542	60	5	algorithm	algorithm	PROPN
ajst-32542	60	6	first	first	ADV
ajst-32542	60	7	decomposes	decompose	VERB
ajst-32542	60	8	the	the	DET
ajst-32542	60	9	gold	gold	NOUN
ajst-32542	60	10	price	price	NOUN
ajst-32542	60	11	time	time	NOUN
ajst-32542	60	12	series	series	PROPN
ajst-32542	60	13	data	data	PROPN
ajst-32542	60	14	,	,	PUNCT
ajst-32542	60	15	obtaining	obtain	VERB
ajst-32542	60	16	multiple	multiple	ADJ
ajst-32542	60	17	imf	imf	ADJ
ajst-32542	60	18	components	component	NOUN
ajst-32542	60	19	at	at	ADP
ajst-32542	60	20	different	different	ADJ
ajst-32542	60	21	frequency	frequency	NOUN
ajst-32542	60	22	scales	scale	NOUN
ajst-32542	60	23	and	and	CCONJ
ajst-32542	60	24	preserving	preserve	VERB
ajst-32542	60	25	the	the	DET
ajst-32542	60	26	primary	primary	ADJ
ajst-32542	60	27	features	feature	NOUN
ajst-32542	60	28	of	of	ADP
ajst-32542	60	29	the	the	DET
ajst-32542	60	30	signal	signal	NOUN
ajst-32542	60	31	by	by	ADP
ajst-32542	60	32	removing	remove	VERB
ajst-32542	60	33	the	the	DET
ajst-32542	60	34	residual	residual	ADJ
ajst-32542	60	35	part	part	NOUN
ajst-32542	60	36	.	.	PUNCT
ajst-32542	61	1	these	these	DET
ajst-32542	61	2	imfs	imfs	NOUN
ajst-32542	61	3	are	be	AUX
ajst-32542	61	4	used	use	VERB
ajst-32542	61	5	as	as	ADP
ajst-32542	61	6	independent	independent	ADJ
ajst-32542	61	7	input	input	NOUN
ajst-32542	61	8	variables	variable	NOUN
ajst-32542	61	9	and	and	CCONJ
ajst-32542	61	10	fed	feed	VERB
ajst-32542	61	11	into	into	ADP
ajst-32542	61	12	the	the	DET
ajst-32542	61	13	xgboost	xgboost	PROPN
ajst-32542	61	14	model	model	NOUN
ajst-32542	61	15	for	for	ADP
ajst-32542	61	16	training	training	NOUN
ajst-32542	61	17	and	and	CCONJ
ajst-32542	61	18	prediction	prediction	NOUN
ajst-32542	61	19	.	.	PUNCT
ajst-32542	62	1	the	the	DET
ajst-32542	62	2	xgboost	xgboost	PROPN
ajst-32542	62	3	model	model	NOUN
ajst-32542	62	4	then	then	ADV
ajst-32542	62	5	uses	use	VERB
ajst-32542	62	6	gradient	gradient	NOUN
ajst-32542	62	7	boosting	boost	VERB
ajst-32542	62	8	methods	method	NOUN
ajst-32542	62	9	for	for	ADP
ajst-32542	62	10	nonlinear	nonlinear	ADJ
ajst-32542	62	11	modeling	modeling	NOUN
ajst-32542	62	12	,	,	PUNCT
ajst-32542	62	13	continuously	continuously	ADV
ajst-32542	62	14	optimizing	optimize	VERB
ajst-32542	62	15	the	the	DET
ajst-32542	62	16	prediction	prediction	NOUN
ajst-32542	62	17	results	result	VERB
ajst-32542	62	18	through	through	ADP
ajst-32542	62	19	tree	tree	NOUN
ajst-32542	62	20	-	-	PUNCT
ajst-32542	62	21	based	base	VERB
ajst-32542	62	22	models	model	NOUN
ajst-32542	62	23	.	.	PUNCT
ajst-32542	63	1	to	to	PART
ajst-32542	63	2	further	far	ADV
ajst-32542	63	3	evaluate	evaluate	VERB
ajst-32542	63	4	the	the	DET
ajst-32542	63	5	effectiveness	effectiveness	NOUN
ajst-32542	63	6	of	of	ADP
ajst-32542	63	7	the	the	DET
ajst-32542	63	8	ceemdan	ceemdan	PROPN
ajst-32542	63	9	and	and	CCONJ
ajst-32542	63	10	xgboost	xgboost	ADV
ajst-32542	63	11	combined	combined	ADJ
ajst-32542	63	12	model	model	NOUN
ajst-32542	63	13	,	,	PUNCT
ajst-32542	63	14	comparative	comparative	ADJ
ajst-32542	63	15	experiments	experiment	NOUN
ajst-32542	63	16	are	be	AUX
ajst-32542	63	17	also	also	ADV
ajst-32542	63	18	conducted	conduct	VERB
ajst-32542	63	19	.	.	PUNCT
ajst-32542	64	1	the	the	DET
ajst-32542	64	2	comparison	comparison	NOUN
ajst-32542	64	3	group	group	NOUN
ajst-32542	64	4	uses	use	VERB
ajst-32542	64	5	traditional	traditional	ADJ
ajst-32542	64	6	single	single	ADJ
ajst-32542	64	7	time	time	NOUN
ajst-32542	64	8	series	series	PROPN
ajst-32542	64	9	forecasting	forecasting	NOUN
ajst-32542	64	10	methods	method	NOUN
ajst-32542	64	11	,	,	PUNCT
ajst-32542	64	12	where	where	SCONJ
ajst-32542	64	13	the	the	DET
ajst-32542	64	14	original	original	ADJ
ajst-32542	64	15	gold	gold	NOUN
ajst-32542	64	16	price	price	NOUN
ajst-32542	64	17	data	datum	NOUN
ajst-32542	64	18	is	be	AUX
ajst-32542	64	19	directly	directly	ADV
ajst-32542	64	20	used	use	VERB
ajst-32542	64	21	to	to	PART
ajst-32542	64	22	train	train	VERB
ajst-32542	64	23	the	the	DET
ajst-32542	64	24	xgboost	xgboost	PROPN
ajst-32542	64	25	model	model	NOUN
ajst-32542	64	26	without	without	ADP
ajst-32542	64	27	the	the	DET
ajst-32542	64	28	ceemdan	ceemdan	PROPN
ajst-32542	64	29	preprocessing	preprocessing	NOUN
ajst-32542	64	30	.	.	PUNCT
ajst-32542	65	1	by	by	ADP
ajst-32542	65	2	comparing	compare	VERB
ajst-32542	65	3	the	the	DET
ajst-32542	65	4	prediction	prediction	NOUN
ajst-32542	65	5	performance	performance	NOUN
ajst-32542	65	6	of	of	ADP
ajst-32542	65	7	these	these	DET
ajst-32542	65	8	two	two	NUM
ajst-32542	65	9	models	model	NOUN
ajst-32542	65	10	,	,	PUNCT
ajst-32542	65	11	the	the	DET
ajst-32542	65	12	impact	impact	NOUN
ajst-32542	65	13	of	of	ADP
ajst-32542	65	14	ceemdan	ceemdan	PROPN
ajst-32542	65	15	on	on	ADP
ajst-32542	65	16	the	the	DET
ajst-32542	65	17	accuracy	accuracy	NOUN
ajst-32542	65	18	and	and	CCONJ
ajst-32542	65	19	stability	stability	NOUN
ajst-32542	65	20	of	of	ADP
ajst-32542	65	21	gold	gold	NOUN
ajst-32542	65	22	price	price	NOUN
ajst-32542	65	23	predictions	prediction	NOUN
ajst-32542	65	24	can	can	AUX
ajst-32542	65	25	be	be	AUX
ajst-32542	65	26	analyzed	analyze	VERB
ajst-32542	65	27	.	.	PUNCT
ajst-32542	66	1	in	in	ADP
ajst-32542	66	2	terms	term	NOUN
ajst-32542	66	3	of	of	ADP
ajst-32542	66	4	evaluation	evaluation	NOUN
ajst-32542	66	5	metrics	metric	NOUN
ajst-32542	66	6	,	,	PUNCT
ajst-32542	66	7	this	this	DET
ajst-32542	66	8	study	study	NOUN
ajst-32542	66	9	employs	employ	VERB
ajst-32542	66	10	several	several	ADJ
ajst-32542	66	11	common	common	ADJ
ajst-32542	66	12	regression	regression	NOUN
ajst-32542	66	13	model	model	NOUN
ajst-32542	66	14	evaluation	evaluation	NOUN
ajst-32542	66	15	methods	method	NOUN
ajst-32542	66	16	,	,	PUNCT
ajst-32542	66	17	including	include	VERB
ajst-32542	66	18	mean	mean	ADJ
ajst-32542	66	19	squared	square	VERB
ajst-32542	66	20	error	error	NOUN
ajst-32542	66	21	(	(	PUNCT
ajst-32542	66	22	mse	mse	NOUN
ajst-32542	66	23	)	)	PUNCT
ajst-32542	66	24	,	,	PUNCT
ajst-32542	66	25	root	root	NOUN
ajst-32542	66	26	mean	mean	VERB
ajst-32542	66	27	squared	square	VERB
ajst-32542	66	28	error	error	NOUN
ajst-32542	66	29	(	(	PUNCT
ajst-32542	66	30	rmse	rmse	NOUN
ajst-32542	66	31	)	)	PUNCT
ajst-32542	66	32	,	,	PUNCT
ajst-32542	66	33	and	and	CCONJ
ajst-32542	66	34	mean	mean	VERB
ajst-32542	66	35	absolute	absolute	ADJ
ajst-32542	66	36	error	error	NOUN
ajst-32542	66	37	(	(	PUNCT
ajst-32542	66	38	mae	mae	PROPN
ajst-32542	66	39	)	)	PUNCT
ajst-32542	66	40	,	,	PUNCT
ajst-32542	66	41	to	to	PART
ajst-32542	66	42	comprehensively	comprehensively	ADV
ajst-32542	66	43	assess	assess	VERB
ajst-32542	66	44	the	the	DET
ajst-32542	66	45	model	model	NOUN
ajst-32542	66	46	's	's	PART
ajst-32542	66	47	prediction	prediction	NOUN
ajst-32542	66	48	accuracy	accuracy	NOUN
ajst-32542	66	49	and	and	CCONJ
ajst-32542	66	50	generalization	generalization	NOUN
ajst-32542	66	51	ability	ability	NOUN
ajst-32542	66	52	.	.	PUNCT
ajst-32542	67	1	4	4	X
ajst-32542	67	2	.	.	X
ajst-32542	67	3	experimental	experimental	ADJ
ajst-32542	67	4	comparative	comparative	ADJ
ajst-32542	67	5	analysis	analysis	NOUN
ajst-32542	67	6	4.1	4.1	NUM
ajst-32542	67	7	data	datum	NOUN
ajst-32542	67	8	decomposition	decomposition	NOUN
ajst-32542	67	9	based	base	VERB
ajst-32542	67	10	on	on	ADP
ajst-32542	67	11	ceemdan	ceemdan	PROPN
ajst-32542	67	12	figure	figure	NOUN
ajst-32542	67	13	2	2	NUM
ajst-32542	67	14	shows	show	VERB
ajst-32542	67	15	the	the	DET
ajst-32542	67	16	intrinsic	intrinsic	ADJ
ajst-32542	67	17	mode	mode	NOUN
ajst-32542	67	18	functions	function	NOUN
ajst-32542	67	19	(	(	PUNCT
ajst-32542	67	20	imfs	imfs	NOUN
ajst-32542	67	21	)	)	PUNCT
ajst-32542	67	22	obtained	obtain	VERB
ajst-32542	67	23	after	after	ADP
ajst-32542	67	24	applying	apply	VERB
ajst-32542	67	25	the	the	DET
ajst-32542	67	26	ceemdan	ceemdan	ADJ
ajst-32542	67	27	algorithm	algorithm	NOUN
ajst-32542	67	28	to	to	PART
ajst-32542	67	29	decompose	decompose	VERB
ajst-32542	67	30	the	the	DET
ajst-32542	67	31	gold	gold	NOUN
ajst-32542	67	32	price	price	NOUN
ajst-32542	67	33	time	time	NOUN
ajst-32542	67	34	series	series	PROPN
ajst-32542	67	35	data	data	PROPN
ajst-32542	67	36	.	.	PUNCT
ajst-32542	68	1	the	the	DET
ajst-32542	68	2	different	different	ADJ
ajst-32542	68	3	imf	imf	PROPN
ajst-32542	68	4	components	component	NOUN
ajst-32542	68	5	in	in	ADP
ajst-32542	68	6	the	the	DET
ajst-32542	68	7	figure	figure	NOUN
ajst-32542	68	8	clearly	clearly	ADV
ajst-32542	68	9	reveal	reveal	VERB
ajst-32542	68	10	the	the	DET
ajst-32542	68	11	changing	change	VERB
ajst-32542	68	12	characteristics	characteristic	NOUN
ajst-32542	68	13	of	of	ADP
ajst-32542	68	14	the	the	DET
ajst-32542	68	15	signal	signal	NOUN
ajst-32542	68	16	at	at	ADP
ajst-32542	68	17	various	various	ADJ
ajst-32542	68	18	frequency	frequency	NOUN
ajst-32542	68	19	scales	scale	NOUN
ajst-32542	68	20	.	.	PUNCT
ajst-32542	69	1	in	in	ADP
ajst-32542	69	2	this	this	DET
ajst-32542	69	3	experiment	experiment	NOUN
ajst-32542	69	4	,	,	PUNCT
ajst-32542	69	5	the	the	DET
ajst-32542	69	6	decomposition	decomposition	NOUN
ajst-32542	69	7	process	process	NOUN
ajst-32542	69	8	uncovers	uncover	NOUN
ajst-32542	69	9	the	the	DET
ajst-32542	69	10	complex	complex	ADJ
ajst-32542	69	11	dynamic	dynamic	ADJ
ajst-32542	69	12	fluctuations	fluctuation	NOUN
ajst-32542	69	13	of	of	ADP
ajst-32542	69	14	gold	gold	NOUN
ajst-32542	69	15	prices	price	NOUN
ajst-32542	69	16	,	,	PUNCT
ajst-32542	69	17	which	which	PRON
ajst-32542	69	18	correspond	correspond	VERB
ajst-32542	69	19	to	to	ADP
ajst-32542	69	20	multiple	multiple	ADJ
ajst-32542	69	21	time	time	NOUN
ajst-32542	69	22	series	series	NOUN
ajst-32542	69	23	components	component	NOUN
ajst-32542	69	24	at	at	ADP
ajst-32542	69	25	various	various	ADJ
ajst-32542	69	26	frequencies	frequency	NOUN
ajst-32542	69	27	.	.	PUNCT
ajst-32542	70	1	from	from	ADP
ajst-32542	70	2	figure	figure	NOUN
ajst-32542	70	3	2	2	NUM
ajst-32542	70	4	,	,	PUNCT
ajst-32542	70	5	it	it	PRON
ajst-32542	70	6	can	can	AUX
ajst-32542	70	7	be	be	AUX
ajst-32542	70	8	observed	observe	VERB
ajst-32542	70	9	that	that	SCONJ
ajst-32542	70	10	imf1	imf1	PROPN
ajst-32542	70	11	and	and	CCONJ
ajst-32542	70	12	imf2	imf2	PROPN
ajst-32542	70	13	correspond	correspond	VERB
ajst-32542	70	14	to	to	ADP
ajst-32542	70	15	high	high	ADJ
ajst-32542	70	16	-	-	PUNCT
ajst-32542	70	17	frequency	frequency	NOUN
ajst-32542	70	18	components	component	NOUN
ajst-32542	70	19	,	,	PUNCT
ajst-32542	70	20	primarily	primarily	ADV
ajst-32542	70	21	reflecting	reflect	VERB
ajst-32542	70	22	the	the	DET
ajst-32542	70	23	details	detail	NOUN
ajst-32542	70	24	of	of	ADP
ajst-32542	70	25	short	short	ADJ
ajst-32542	70	26	-	-	PUNCT
ajst-32542	70	27	term	term	NOUN
ajst-32542	70	28	fluctuations	fluctuation	NOUN
ajst-32542	70	29	.	.	PUNCT
ajst-32542	71	1	imf3	imf3	PROPN
ajst-32542	71	2	represents	represent	VERB
ajst-32542	71	3	mid	mid	ADJ
ajst-32542	71	4	-	-	ADJ
ajst-32542	71	5	frequency	frequency	ADJ
ajst-32542	71	6	components	component	NOUN
ajst-32542	71	7	,	,	PUNCT
ajst-32542	71	8	capturing	capture	VERB
ajst-32542	71	9	relatively	relatively	ADV
ajst-32542	71	10	stable	stable	ADJ
ajst-32542	71	11	trend	trend	NOUN
ajst-32542	71	12	changes	change	NOUN
ajst-32542	71	13	,	,	PUNCT
ajst-32542	71	14	while	while	SCONJ
ajst-32542	71	15	imf4	imf4	PROPN
ajst-32542	71	16	presents	present	VERB
ajst-32542	71	17	low	low	ADJ
ajst-32542	71	18	-	-	PUNCT
ajst-32542	71	19	frequency	frequency	NOUN
ajst-32542	71	20	long	long	ADJ
ajst-32542	71	21	-	-	PUNCT
ajst-32542	71	22	term	term	NOUN
ajst-32542	71	23	trend	trend	NOUN
ajst-32542	71	24	components	component	NOUN
ajst-32542	71	25	.	.	PUNCT
ajst-32542	72	1	each	each	DET
ajst-32542	72	2	imf	imf	PROPN
ajst-32542	72	3	layer	layer	NOUN
ajst-32542	72	4	effectively	effectively	ADV
ajst-32542	72	5	reveals	reveal	VERB
ajst-32542	72	6	different	different	ADJ
ajst-32542	72	7	feature	feature	NOUN
ajst-32542	72	8	levels	level	NOUN
ajst-32542	72	9	in	in	ADP
ajst-32542	72	10	the	the	DET
ajst-32542	72	11	original	original	ADJ
ajst-32542	72	12	signal	signal	NOUN
ajst-32542	72	13	,	,	PUNCT
ajst-32542	72	14	fully	fully	ADV
ajst-32542	72	15	demonstrating	demonstrate	VERB
ajst-32542	72	16	the	the	DET
ajst-32542	72	17	advantages	advantage	NOUN
ajst-32542	72	18	of	of	ADP
ajst-32542	72	19	ceemdan	ceemdan	NOUN
ajst-32542	72	20	in	in	ADP
ajst-32542	72	21	extracting	extract	VERB
ajst-32542	72	22	nonlinear	nonlinear	ADJ
ajst-32542	72	23	and	and	CCONJ
ajst-32542	72	24	non	non	ADJ
ajst-32542	72	25	-	-	ADJ
ajst-32542	72	26	stationary	stationary	ADJ
ajst-32542	72	27	data	data	NOUN
ajst-32542	72	28	features	feature	NOUN
ajst-32542	72	29	.	.	PUNCT
ajst-32542	73	1	92	92	NUM
ajst-32542	73	2	figure	figure	NOUN
ajst-32542	73	3	2	2	NUM
ajst-32542	73	4	.	.	PUNCT
ajst-32542	73	5	intrinsic	intrinsic	ADJ
ajst-32542	73	6	mode	mode	NOUN
ajst-32542	73	7	functions	function	NOUN
ajst-32542	73	8	(	(	PUNCT
ajst-32542	73	9	imfs	imfs	NOUN
ajst-32542	73	10	)	)	PUNCT
ajst-32542	73	11	after	after	ADP
ajst-32542	73	12	ceemdan	ceemdan	ADJ
ajst-32542	73	13	decomposition	decomposition	NOUN
ajst-32542	73	14	through	through	ADP
ajst-32542	73	15	this	this	DET
ajst-32542	73	16	decomposition	decomposition	NOUN
ajst-32542	73	17	,	,	PUNCT
ajst-32542	73	18	noise	noise	NOUN
ajst-32542	73	19	and	and	CCONJ
ajst-32542	73	20	the	the	DET
ajst-32542	73	21	true	true	ADJ
ajst-32542	73	22	trend	trend	NOUN
ajst-32542	73	23	in	in	ADP
ajst-32542	73	24	the	the	DET
ajst-32542	73	25	original	original	ADJ
ajst-32542	73	26	data	datum	NOUN
ajst-32542	73	27	are	be	AUX
ajst-32542	73	28	separated	separate	VERB
ajst-32542	73	29	,	,	PUNCT
ajst-32542	73	30	providing	provide	VERB
ajst-32542	73	31	clearer	clear	ADJ
ajst-32542	73	32	and	and	CCONJ
ajst-32542	73	33	more	more	ADV
ajst-32542	73	34	meaningful	meaningful	ADJ
ajst-32542	73	35	input	input	NOUN
ajst-32542	73	36	features	feature	NOUN
ajst-32542	73	37	for	for	ADP
ajst-32542	73	38	the	the	DET
ajst-32542	73	39	subsequent	subsequent	ADJ
ajst-32542	73	40	prediction	prediction	NOUN
ajst-32542	73	41	model	model	NOUN
ajst-32542	73	42	.	.	PUNCT
ajst-32542	74	1	these	these	DET
ajst-32542	74	2	decomposed	decompose	VERB
ajst-32542	74	3	signal	signal	NOUN
ajst-32542	74	4	components	component	NOUN
ajst-32542	74	5	will	will	AUX
ajst-32542	74	6	be	be	AUX
ajst-32542	74	7	used	use	VERB
ajst-32542	74	8	as	as	ADP
ajst-32542	74	9	input	input	NOUN
ajst-32542	74	10	data	datum	NOUN
ajst-32542	74	11	for	for	ADP
ajst-32542	74	12	the	the	DET
ajst-32542	74	13	xgboost	xgboost	PROPN
ajst-32542	74	14	model	model	PROPN
ajst-32542	74	15	,	,	PUNCT
ajst-32542	74	16	enabling	enable	VERB
ajst-32542	74	17	the	the	DET
ajst-32542	74	18	model	model	NOUN
ajst-32542	74	19	to	to	PART
ajst-32542	74	20	more	more	ADV
ajst-32542	74	21	accurately	accurately	ADV
ajst-32542	74	22	capture	capture	VERB
ajst-32542	74	23	the	the	DET
ajst-32542	74	24	changing	change	VERB
ajst-32542	74	25	trends	trend	NOUN
ajst-32542	74	26	of	of	ADP
ajst-32542	74	27	each	each	DET
ajst-32542	74	28	frequency	frequency	NOUN
ajst-32542	74	29	component	component	NOUN
ajst-32542	74	30	when	when	SCONJ
ajst-32542	74	31	predicting	predict	VERB
ajst-32542	74	32	gold	gold	NOUN
ajst-32542	74	33	prices	price	NOUN
ajst-32542	74	34	.	.	PUNCT
ajst-32542	75	1	4.2	4.2	NUM
ajst-32542	75	2	xgboost	xgboost	NOUN
ajst-32542	75	3	prediction	prediction	NOUN
ajst-32542	75	4	figure	figure	NOUN
ajst-32542	75	5	3	3	NUM
ajst-32542	75	6	.	.	NOUN
ajst-32542	75	7	comparison	comparison	NOUN
ajst-32542	75	8	of	of	ADP
ajst-32542	75	9	predicted	predict	VERB
ajst-32542	75	10	and	and	CCONJ
ajst-32542	75	11	actual	actual	ADJ
ajst-32542	75	12	values	value	NOUN
ajst-32542	75	13	for	for	ADP
ajst-32542	75	14	the	the	DET
ajst-32542	75	15	xgboost	xgboost	PROPN
ajst-32542	75	16	model	model	NOUN
ajst-32542	75	17	on	on	ADP
ajst-32542	75	18	the	the	DET
ajst-32542	75	19	test	test	NOUN
ajst-32542	75	20	set	set	VERB
ajst-32542	75	21	0	0	NUM
ajst-32542	75	22	0.05	0.05	NUM
ajst-32542	75	23	0.1	0.1	NUM
ajst-32542	75	24	0.15	0.15	NUM
ajst-32542	75	25	0.2	0.2	NUM
ajst-32542	75	26	0.25	0.25	NUM
ajst-32542	75	27	0.3	0.3	NUM
ajst-32542	75	28	0.35	0.35	NUM
ajst-32542	75	29	0.4	0.4	NUM
ajst-32542	75	30	0.45	0.45	NUM
ajst-32542	75	31	0.5	0.5	NUM
ajst-32542	75	32	0	0	NUM
ajst-32542	75	33	2	2	NUM
ajst-32542	75	34	0	0	NUM
ajst-32542	75	35	0.05	0.05	NUM
ajst-32542	75	36	0.1	0.1	NUM
ajst-32542	75	37	0.15	0.15	NUM
ajst-32542	75	38	0.2	0.2	NUM
ajst-32542	75	39	0.25	0.25	NUM
ajst-32542	75	40	0.3	0.3	NUM
ajst-32542	75	41	0.35	0.35	NUM
ajst-32542	75	42	0.4	0.4	NUM
ajst-32542	75	43	0.45	0.45	NUM
ajst-32542	75	44	0.5	0.5	NUM
ajst-32542	75	45	0	0	NUM
ajst-32542	75	46	5	5	NUM
ajst-32542	75	47	0	0	NUM
ajst-32542	75	48	0.05	0.05	NUM
ajst-32542	75	49	0.1	0.1	NUM
ajst-32542	75	50	0.15	0.15	NUM
ajst-32542	75	51	0.2	0.2	NUM
ajst-32542	75	52	0.25	0.25	NUM
ajst-32542	75	53	0.3	0.3	NUM
ajst-32542	75	54	0.35	0.35	NUM
ajst-32542	75	55	0.4	0.4	NUM
ajst-32542	75	56	0.45	0.45	NUM
ajst-32542	75	57	0.5	0.5	NUM
ajst-32542	75	58	0	0	NUM
ajst-32542	75	59	200	200	NUM
ajst-32542	75	60	0	0	NUM
ajst-32542	75	61	0.05	0.05	NUM
ajst-32542	75	62	0.1	0.1	NUM
ajst-32542	75	63	0.15	0.15	NUM
ajst-32542	75	64	0.2	0.2	NUM
ajst-32542	75	65	0.25	0.25	NUM
ajst-32542	75	66	0.3	0.3	NUM
ajst-32542	75	67	0.35	0.35	NUM
ajst-32542	75	68	0.4	0.4	NUM
ajst-32542	75	69	0.45	0.45	NUM
ajst-32542	75	70	0.5	0.5	NUM
ajst-32542	75	71	0	0	NUM
ajst-32542	75	72	0.5	0.5	NUM
ajst-32542	75	73	1	1	NUM
ajst-32542	75	74	0	0	NUM
ajst-32542	75	75	20	20	NUM
ajst-32542	75	76	40	40	NUM
ajst-32542	75	77	60	60	NUM
ajst-32542	75	78	80	80	NUM
ajst-32542	75	79	100	100	NUM
ajst-32542	75	80	sample	sample	NOUN
ajst-32542	75	81	1750	1750	NUM
ajst-32542	75	82	1800	1800	NUM
ajst-32542	75	83	1850	1850	NUM
ajst-32542	75	84	1900	1900	NUM
ajst-32542	75	85	1950	1950	NUM
ajst-32542	75	86	2000	2000	NUM
ajst-32542	75	87	2050	2050	NUM
ajst-32542	75	88	2100	2100	NUM
ajst-32542	75	89	true	true	ADJ
ajst-32542	75	90	predict	predict	VERB
ajst-32542	75	91	93	93	NUM
ajst-32542	75	92	figure	figure	NOUN
ajst-32542	75	93	3	3	NUM
ajst-32542	75	94	illustrates	illustrate	VERB
ajst-32542	75	95	the	the	DET
ajst-32542	75	96	results	result	NOUN
ajst-32542	75	97	of	of	ADP
ajst-32542	75	98	gold	gold	NOUN
ajst-32542	75	99	price	price	NOUN
ajst-32542	75	100	prediction	prediction	NOUN
ajst-32542	75	101	using	use	VERB
ajst-32542	75	102	the	the	DET
ajst-32542	75	103	xgboost	xgboost	PROPN
ajst-32542	75	104	model	model	NOUN
ajst-32542	75	105	,	,	PUNCT
ajst-32542	75	106	where	where	SCONJ
ajst-32542	75	107	the	the	DET
ajst-32542	75	108	actual	actual	ADJ
ajst-32542	75	109	values	value	NOUN
ajst-32542	75	110	are	be	AUX
ajst-32542	75	111	represented	represent	VERB
ajst-32542	75	112	as	as	ADP
ajst-32542	75	113	scatter	scatter	NOUN
ajst-32542	75	114	points	point	NOUN
ajst-32542	75	115	and	and	CCONJ
ajst-32542	75	116	the	the	DET
ajst-32542	75	117	predicted	predict	VERB
ajst-32542	75	118	values	value	NOUN
ajst-32542	75	119	are	be	AUX
ajst-32542	75	120	shown	show	VERB
ajst-32542	75	121	as	as	ADP
ajst-32542	75	122	a	a	DET
ajst-32542	75	123	continuous	continuous	ADJ
ajst-32542	75	124	line	line	NOUN
ajst-32542	75	125	.	.	PUNCT
ajst-32542	76	1	from	from	ADP
ajst-32542	76	2	the	the	DET
ajst-32542	76	3	figure	figure	NOUN
ajst-32542	76	4	,	,	PUNCT
ajst-32542	76	5	it	it	PRON
ajst-32542	76	6	is	be	AUX
ajst-32542	76	7	evident	evident	ADJ
ajst-32542	76	8	that	that	SCONJ
ajst-32542	76	9	the	the	DET
ajst-32542	76	10	prediction	prediction	NOUN
ajst-32542	76	11	curve	curve	NOUN
ajst-32542	76	12	closely	closely	ADV
ajst-32542	76	13	follows	follow	VERB
ajst-32542	76	14	the	the	DET
ajst-32542	76	15	real	real	ADJ
ajst-32542	76	16	price	price	NOUN
ajst-32542	76	17	changes	change	NOUN
ajst-32542	76	18	,	,	PUNCT
ajst-32542	76	19	effectively	effectively	ADV
ajst-32542	76	20	capturing	capture	VERB
ajst-32542	76	21	the	the	DET
ajst-32542	76	22	main	main	ADJ
ajst-32542	76	23	features	feature	NOUN
ajst-32542	76	24	of	of	ADP
ajst-32542	76	25	gold	gold	ADJ
ajst-32542	76	26	price	price	NOUN
ajst-32542	76	27	fluctuations	fluctuation	NOUN
ajst-32542	76	28	in	in	ADP
ajst-32542	76	29	both	both	CCONJ
ajst-32542	76	30	short	short	ADJ
ajst-32542	76	31	-	-	PUNCT
ajst-32542	76	32	term	term	NOUN
ajst-32542	76	33	volatility	volatility	NOUN
ajst-32542	76	34	and	and	CCONJ
ajst-32542	76	35	medium	medium	ADJ
ajst-32542	76	36	-	-	PUNCT
ajst-32542	76	37	tolong	tolong	NOUN
ajst-32542	76	38	-	-	PUNCT
ajst-32542	76	39	term	term	NOUN
ajst-32542	76	40	trends	trend	NOUN
ajst-32542	76	41	.	.	PUNCT
ajst-32542	77	1	although	although	SCONJ
ajst-32542	77	2	there	there	PRON
ajst-32542	77	3	are	be	VERB
ajst-32542	77	4	some	some	DET
ajst-32542	77	5	deviations	deviation	NOUN
ajst-32542	77	6	in	in	ADP
ajst-32542	77	7	regions	region	NOUN
ajst-32542	77	8	of	of	ADP
ajst-32542	77	9	high	high	ADJ
ajst-32542	77	10	volatility	volatility	NOUN
ajst-32542	77	11	,	,	PUNCT
ajst-32542	77	12	the	the	DET
ajst-32542	77	13	overall	overall	ADJ
ajst-32542	77	14	trend	trend	NOUN
ajst-32542	77	15	remains	remain	VERB
ajst-32542	77	16	consistent	consistent	ADJ
ajst-32542	77	17	,	,	PUNCT
ajst-32542	77	18	demonstrating	demonstrate	VERB
ajst-32542	77	19	that	that	SCONJ
ajst-32542	77	20	the	the	DET
ajst-32542	77	21	xgboost	xgboost	PROPN
ajst-32542	77	22	model	model	NOUN
ajst-32542	77	23	has	have	VERB
ajst-32542	77	24	strong	strong	ADJ
ajst-32542	77	25	fitting	fitting	ADJ
ajst-32542	77	26	capability	capability	NOUN
ajst-32542	77	27	and	and	CCONJ
ajst-32542	77	28	predictive	predictive	ADJ
ajst-32542	77	29	reliability	reliability	NOUN
ajst-32542	77	30	when	when	SCONJ
ajst-32542	77	31	handling	handle	VERB
ajst-32542	77	32	nonlinear	nonlinear	ADJ
ajst-32542	77	33	financial	financial	ADJ
ajst-32542	77	34	time	time	NOUN
ajst-32542	77	35	series	series	PROPN
ajst-32542	77	36	data	data	PROPN
ajst-32542	77	37	.	.	PUNCT
ajst-32542	78	1	to	to	PART
ajst-32542	78	2	further	far	ADV
ajst-32542	78	3	quantify	quantify	VERB
ajst-32542	78	4	the	the	DET
ajst-32542	78	5	model	model	NOUN
ajst-32542	78	6	's	's	PART
ajst-32542	78	7	performance	performance	NOUN
ajst-32542	78	8	,	,	PUNCT
ajst-32542	78	9	this	this	DET
ajst-32542	78	10	study	study	NOUN
ajst-32542	78	11	calculates	calculate	VERB
ajst-32542	78	12	the	the	DET
ajst-32542	78	13	prediction	prediction	NOUN
ajst-32542	78	14	metrics	metric	NOUN
ajst-32542	78	15	for	for	ADP
ajst-32542	78	16	the	the	DET
ajst-32542	78	17	training	training	NOUN
ajst-32542	78	18	set	set	NOUN
ajst-32542	78	19	,	,	PUNCT
ajst-32542	78	20	validation	validation	NOUN
ajst-32542	78	21	set	set	NOUN
ajst-32542	78	22	,	,	PUNCT
ajst-32542	78	23	and	and	CCONJ
ajst-32542	78	24	test	test	NOUN
ajst-32542	78	25	set	set	VERB
ajst-32542	78	26	separately	separately	ADV
ajst-32542	78	27	.	.	PUNCT
ajst-32542	79	1	table	table	NOUN
ajst-32542	79	2	1	1	NUM
ajst-32542	79	3	presents	present	VERB
ajst-32542	79	4	the	the	DET
ajst-32542	79	5	evaluation	evaluation	NOUN
ajst-32542	79	6	results	result	NOUN
ajst-32542	79	7	of	of	ADP
ajst-32542	79	8	the	the	DET
ajst-32542	79	9	xgboost	xgboost	PROPN
ajst-32542	79	10	model	model	NOUN
ajst-32542	79	11	on	on	ADP
ajst-32542	79	12	each	each	DET
ajst-32542	79	13	dataset	dataset	NOUN
ajst-32542	79	14	.	.	PUNCT
ajst-32542	80	1	during	during	ADP
ajst-32542	80	2	the	the	DET
ajst-32542	80	3	training	training	NOUN
ajst-32542	80	4	phase	phase	NOUN
ajst-32542	80	5	,	,	PUNCT
ajst-32542	80	6	the	the	DET
ajst-32542	80	7	model	model	NOUN
ajst-32542	80	8	demonstrates	demonstrate	VERB
ajst-32542	80	9	high	high	ADJ
ajst-32542	80	10	precision	precision	NOUN
ajst-32542	80	11	,	,	PUNCT
ajst-32542	80	12	with	with	ADP
ajst-32542	80	13	a	a	DET
ajst-32542	80	14	mae	mae	PROPN
ajst-32542	80	15	of	of	ADP
ajst-32542	80	16	7.1615	7.1615	NUM
ajst-32542	80	17	,	,	PUNCT
ajst-32542	80	18	mape	mape	NOUN
ajst-32542	80	19	of	of	ADP
ajst-32542	80	20	0.0053829	0.0053829	NUM
ajst-32542	80	21	,	,	PUNCT
ajst-32542	80	22	rmse	rmse	NOUN
ajst-32542	80	23	of	of	ADP
ajst-32542	80	24	9.6414	9.6414	NUM
ajst-32542	80	25	,	,	PUNCT
ajst-32542	80	26	and	and	CCONJ
ajst-32542	80	27	an	an	DET
ajst-32542	80	28	r²	r²	NOUN
ajst-32542	80	29	of	of	ADP
ajst-32542	80	30	0.99745	0.99745	NUM
ajst-32542	80	31	,	,	PUNCT
ajst-32542	80	32	indicating	indicate	VERB
ajst-32542	80	33	that	that	SCONJ
ajst-32542	80	34	the	the	DET
ajst-32542	80	35	model	model	NOUN
ajst-32542	80	36	has	have	AUX
ajst-32542	80	37	achieved	achieve	VERB
ajst-32542	80	38	a	a	DET
ajst-32542	80	39	high	high	ADJ
ajst-32542	80	40	degree	degree	NOUN
ajst-32542	80	41	of	of	ADP
ajst-32542	80	42	accuracy	accuracy	NOUN
ajst-32542	80	43	in	in	ADP
ajst-32542	80	44	fitting	fit	VERB
ajst-32542	80	45	the	the	DET
ajst-32542	80	46	training	training	NOUN
ajst-32542	80	47	data	datum	NOUN
ajst-32542	80	48	.	.	PUNCT
ajst-32542	81	1	the	the	DET
ajst-32542	81	2	validation	validation	NOUN
ajst-32542	81	3	set	set	NOUN
ajst-32542	81	4	results	result	NOUN
ajst-32542	81	5	show	show	VERB
ajst-32542	81	6	a	a	DET
ajst-32542	81	7	mae	mae	PROPN
ajst-32542	81	8	of	of	ADP
ajst-32542	81	9	20.0696	20.0696	NUM
ajst-32542	81	10	,	,	PUNCT
ajst-32542	81	11	rmse	rmse	NOUN
ajst-32542	81	12	of	of	ADP
ajst-32542	81	13	26.0623	26.0623	NUM
ajst-32542	81	14	,	,	PUNCT
ajst-32542	81	15	and	and	CCONJ
ajst-32542	81	16	r²	r²	VERB
ajst-32542	81	17	of	of	ADP
ajst-32542	81	18	0.79376	0.79376	NUM
ajst-32542	81	19	.	.	PUNCT
ajst-32542	82	1	while	while	SCONJ
ajst-32542	82	2	the	the	DET
ajst-32542	82	3	error	error	NOUN
ajst-32542	82	4	is	be	AUX
ajst-32542	82	5	slightly	slightly	ADV
ajst-32542	82	6	higher	high	ADJ
ajst-32542	82	7	compared	compare	VERB
ajst-32542	82	8	to	to	ADP
ajst-32542	82	9	the	the	DET
ajst-32542	82	10	training	training	NOUN
ajst-32542	82	11	set	set	NOUN
ajst-32542	82	12	,	,	PUNCT
ajst-32542	82	13	the	the	DET
ajst-32542	82	14	model	model	NOUN
ajst-32542	82	15	still	still	ADV
ajst-32542	82	16	maintains	maintain	VERB
ajst-32542	82	17	a	a	DET
ajst-32542	82	18	relatively	relatively	ADV
ajst-32542	82	19	high	high	ADJ
ajst-32542	82	20	explanatory	explanatory	ADJ
ajst-32542	82	21	power	power	NOUN
ajst-32542	82	22	,	,	PUNCT
ajst-32542	82	23	indicating	indicate	VERB
ajst-32542	82	24	strong	strong	ADJ
ajst-32542	82	25	generalization	generalization	NOUN
ajst-32542	82	26	ability	ability	NOUN
ajst-32542	82	27	on	on	ADP
ajst-32542	82	28	unseen	unseen	ADJ
ajst-32542	82	29	data	datum	NOUN
ajst-32542	82	30	.	.	PUNCT
ajst-32542	83	1	the	the	DET
ajst-32542	83	2	test	test	NOUN
ajst-32542	83	3	set	set	NOUN
ajst-32542	83	4	results	result	NOUN
ajst-32542	83	5	,	,	PUNCT
ajst-32542	83	6	with	with	ADP
ajst-32542	83	7	a	a	DET
ajst-32542	83	8	mae	mae	PROPN
ajst-32542	83	9	of	of	ADP
ajst-32542	83	10	21.0753	21.0753	NUM
ajst-32542	83	11	,	,	PUNCT
ajst-32542	83	12	mape	mape	NOUN
ajst-32542	83	13	of	of	ADP
ajst-32542	83	14	0.011639	0.011639	NUM
ajst-32542	83	15	,	,	PUNCT
ajst-32542	83	16	rmse	rmse	NOUN
ajst-32542	83	17	of	of	ADP
ajst-32542	83	18	26.4743	26.4743	NUM
ajst-32542	83	19	,	,	PUNCT
ajst-32542	83	20	and	and	CCONJ
ajst-32542	83	21	an	an	DET
ajst-32542	83	22	r²	r²	NOUN
ajst-32542	83	23	of	of	ADP
ajst-32542	83	24	0.90556	0.90556	NUM
ajst-32542	83	25	,	,	PUNCT
ajst-32542	83	26	further	far	ADV
ajst-32542	83	27	confirm	confirm	VERB
ajst-32542	83	28	the	the	DET
ajst-32542	83	29	model	model	NOUN
ajst-32542	83	30	's	's	PART
ajst-32542	83	31	robustness	robustness	NOUN
ajst-32542	83	32	in	in	ADP
ajst-32542	83	33	realworld	realworld	PROPN
ajst-32542	83	34	prediction	prediction	NOUN
ajst-32542	83	35	scenarios	scenario	NOUN
ajst-32542	83	36	.	.	PUNCT
ajst-32542	84	1	overall	overall	ADJ
ajst-32542	84	2	,	,	PUNCT
ajst-32542	84	3	xgboost	xgboost	PROPN
ajst-32542	84	4	shows	show	VERB
ajst-32542	84	5	strong	strong	ADJ
ajst-32542	84	6	fitting	fitting	ADJ
ajst-32542	84	7	performance	performance	NOUN
ajst-32542	84	8	during	during	ADP
ajst-32542	84	9	the	the	DET
ajst-32542	84	10	training	training	NOUN
ajst-32542	84	11	phase	phase	NOUN
ajst-32542	84	12	and	and	CCONJ
ajst-32542	84	13	maintains	maintain	VERB
ajst-32542	84	14	good	good	ADJ
ajst-32542	84	15	prediction	prediction	NOUN
ajst-32542	84	16	accuracy	accuracy	NOUN
ajst-32542	84	17	in	in	ADP
ajst-32542	84	18	both	both	CCONJ
ajst-32542	84	19	the	the	DET
ajst-32542	84	20	validation	validation	NOUN
ajst-32542	84	21	and	and	CCONJ
ajst-32542	84	22	test	test	NOUN
ajst-32542	84	23	phases	phase	NOUN
ajst-32542	84	24	,	,	PUNCT
ajst-32542	84	25	suggesting	suggest	VERB
ajst-32542	84	26	its	its	PRON
ajst-32542	84	27	ability	ability	NOUN
ajst-32542	84	28	to	to	PART
ajst-32542	84	29	effectively	effectively	ADV
ajst-32542	84	30	model	model	VERB
ajst-32542	84	31	the	the	DET
ajst-32542	84	32	nonlinear	nonlinear	ADJ
ajst-32542	84	33	dynamic	dynamic	ADJ
ajst-32542	84	34	structure	structure	NOUN
ajst-32542	84	35	of	of	ADP
ajst-32542	84	36	gold	gold	NOUN
ajst-32542	84	37	prices	price	NOUN
ajst-32542	84	38	,	,	PUNCT
ajst-32542	84	39	making	make	VERB
ajst-32542	84	40	it	it	PRON
ajst-32542	84	41	valuable	valuable	ADJ
ajst-32542	84	42	for	for	ADP
ajst-32542	84	43	practical	practical	ADJ
ajst-32542	84	44	applications	application	NOUN
ajst-32542	84	45	.	.	PUNCT
ajst-32542	85	1	table	table	NOUN
ajst-32542	85	2	1	1	NUM
ajst-32542	85	3	.	.	PUNCT
ajst-32542	85	4	evaluation	evaluation	NOUN
ajst-32542	85	5	metrics	metric	NOUN
ajst-32542	85	6	of	of	ADP
ajst-32542	85	7	the	the	DET
ajst-32542	85	8	xgboost	xgboost	PROPN
ajst-32542	85	9	model	model	NOUN
ajst-32542	85	10	on	on	ADP
ajst-32542	85	11	different	different	ADJ
ajst-32542	85	12	datasets	dataset	NOUN
ajst-32542	85	13	dataset	dataset	VERB
ajst-32542	85	14	mae	mae	PROPN
ajst-32542	85	15	mape	mape	PROPN
ajst-32542	85	16	mse	mse	PROPN
ajst-32542	85	17	rmse	rmse	PROPN
ajst-32542	85	18	r²	r²	NOUN
ajst-32542	85	19	training	training	NOUN
ajst-32542	85	20	set	set	VERB
ajst-32542	85	21	7.1615	7.1615	NUM
ajst-32542	85	22	0.00538	0.00538	NUM
ajst-32542	85	23	92.9563	92.9563	NUM
ajst-32542	85	24	9.6414	9.6414	NUM
ajst-32542	85	25	0.99745	0.99745	NUM
ajst-32542	85	26	validation	validation	NOUN
ajst-32542	85	27	set	set	VERB
ajst-32542	85	28	20.0696	20.0696	NUM
ajst-32542	85	29	0.01114	0.01114	NUM
ajst-32542	85	30	679.2455	679.2455	NUM
ajst-32542	85	31	26.0623	26.0623	NUM
ajst-32542	86	1	0.79376	0.79376	NUM
ajst-32542	86	2	test	test	NOUN
ajst-32542	86	3	set	set	VERB
ajst-32542	86	4	21.0753	21.0753	NUM
ajst-32542	86	5	0.01164	0.01164	NOUN
ajst-32542	86	6	700.8899	700.8899	NUM
ajst-32542	86	7	26.4743	26.4743	NUM
ajst-32542	86	8	0.90556	0.90556	NUM
ajst-32542	86	9	4.3	4.3	NUM
ajst-32542	86	10	ablation	ablation	NOUN
ajst-32542	86	11	study	study	NOUN
ajst-32542	86	12	in	in	ADP
ajst-32542	86	13	this	this	DET
ajst-32542	86	14	section	section	NOUN
ajst-32542	86	15	,	,	PUNCT
ajst-32542	86	16	we	we	PRON
ajst-32542	86	17	conducted	conduct	VERB
ajst-32542	86	18	an	an	DET
ajst-32542	86	19	ablation	ablation	NOUN
ajst-32542	86	20	experiment	experiment	NOUN
ajst-32542	86	21	to	to	PART
ajst-32542	86	22	compare	compare	VERB
ajst-32542	86	23	the	the	DET
ajst-32542	86	24	performance	performance	NOUN
ajst-32542	86	25	of	of	ADP
ajst-32542	86	26	two	two	NUM
ajst-32542	86	27	models	model	NOUN
ajst-32542	86	28	:	:	PUNCT
ajst-32542	86	29	one	one	NUM
ajst-32542	86	30	that	that	PRON
ajst-32542	86	31	directly	directly	ADV
ajst-32542	86	32	uses	use	VERB
ajst-32542	86	33	xgboost	xgboost	ADV
ajst-32542	86	34	for	for	ADP
ajst-32542	86	35	modeling	modeling	NOUN
ajst-32542	86	36	and	and	CCONJ
ajst-32542	86	37	another	another	PRON
ajst-32542	86	38	that	that	PRON
ajst-32542	86	39	incorporates	incorporate	VERB
ajst-32542	86	40	ceemdan	ceemdan	ADJ
ajst-32542	86	41	for	for	ADP
ajst-32542	86	42	signal	signal	ADJ
ajst-32542	86	43	decomposition	decomposition	NOUN
ajst-32542	86	44	before	before	ADP
ajst-32542	86	45	applying	apply	VERB
ajst-32542	86	46	xgboost	xgboost	ADV
ajst-32542	86	47	.	.	PUNCT
ajst-32542	87	1	this	this	DET
ajst-32542	87	2	comparison	comparison	NOUN
ajst-32542	87	3	helps	help	VERB
ajst-32542	87	4	assess	assess	VERB
ajst-32542	87	5	the	the	DET
ajst-32542	87	6	improvement	improvement	NOUN
ajst-32542	87	7	in	in	ADP
ajst-32542	87	8	xgboost	xgboost	PROPN
ajst-32542	87	9	's	's	PART
ajst-32542	87	10	performance	performance	NOUN
ajst-32542	87	11	when	when	SCONJ
ajst-32542	87	12	ceemdan	ceemdan	PROPN
ajst-32542	87	13	is	be	AUX
ajst-32542	87	14	introduced	introduce	VERB
ajst-32542	87	15	.	.	PUNCT
ajst-32542	88	1	first	first	ADV
ajst-32542	88	2	,	,	PUNCT
ajst-32542	88	3	when	when	SCONJ
ajst-32542	88	4	training	train	VERB
ajst-32542	88	5	the	the	DET
ajst-32542	88	6	xgboost	xgboost	PROPN
ajst-32542	88	7	model	model	NOUN
ajst-32542	88	8	directly	directly	ADV
ajst-32542	88	9	,	,	PUNCT
ajst-32542	88	10	the	the	DET
ajst-32542	88	11	training	training	NOUN
ajst-32542	88	12	set	set	NOUN
ajst-32542	88	13	shows	show	VERB
ajst-32542	88	14	excellent	excellent	ADJ
ajst-32542	88	15	prediction	prediction	NOUN
ajst-32542	88	16	accuracy	accuracy	NOUN
ajst-32542	88	17	with	with	ADP
ajst-32542	88	18	a	a	DET
ajst-32542	88	19	mae	mae	PROPN
ajst-32542	88	20	of	of	ADP
ajst-32542	88	21	3.6907	3.6907	NUM
ajst-32542	88	22	,	,	PUNCT
ajst-32542	88	23	mape	mape	NOUN
ajst-32542	88	24	of	of	ADP
ajst-32542	88	25	0.0027677	0.0027677	NUM
ajst-32542	88	26	,	,	PUNCT
ajst-32542	88	27	rmse	rmse	NOUN
ajst-32542	88	28	of	of	ADP
ajst-32542	88	29	4.7473	4.7473	NUM
ajst-32542	88	30	,	,	PUNCT
ajst-32542	88	31	and	and	CCONJ
ajst-32542	88	32	r²	r²	VERB
ajst-32542	88	33	of	of	ADP
ajst-32542	88	34	0.99938	0.99938	NUM
ajst-32542	88	35	,	,	PUNCT
ajst-32542	88	36	indicating	indicate	VERB
ajst-32542	88	37	that	that	SCONJ
ajst-32542	88	38	the	the	DET
ajst-32542	88	39	model	model	NOUN
ajst-32542	88	40	fits	fit	VERB
ajst-32542	88	41	the	the	DET
ajst-32542	88	42	training	training	NOUN
ajst-32542	88	43	data	datum	NOUN
ajst-32542	88	44	very	very	ADV
ajst-32542	88	45	well	well	ADV
ajst-32542	88	46	,	,	PUNCT
ajst-32542	88	47	as	as	SCONJ
ajst-32542	88	48	shown	show	VERB
ajst-32542	88	49	in	in	ADP
ajst-32542	88	50	table	table	NOUN
ajst-32542	88	51	2	2	NUM
ajst-32542	88	52	.	.	PUNCT
ajst-32542	89	1	however	however	ADV
ajst-32542	89	2	,	,	PUNCT
ajst-32542	89	3	the	the	DET
ajst-32542	89	4	performance	performance	NOUN
ajst-32542	89	5	on	on	ADP
ajst-32542	89	6	the	the	DET
ajst-32542	89	7	validation	validation	NOUN
ajst-32542	89	8	and	and	CCONJ
ajst-32542	89	9	testing	testing	NOUN
ajst-32542	89	10	sets	set	VERB
ajst-32542	89	11	drops	drop	NOUN
ajst-32542	89	12	compared	compare	VERB
ajst-32542	89	13	to	to	ADP
ajst-32542	89	14	the	the	DET
ajst-32542	89	15	training	training	NOUN
ajst-32542	89	16	set	set	NOUN
ajst-32542	89	17	.	.	PUNCT
ajst-32542	90	1	the	the	DET
ajst-32542	90	2	mae	mae	PROPN
ajst-32542	90	3	for	for	ADP
ajst-32542	90	4	the	the	DET
ajst-32542	90	5	validation	validation	NOUN
ajst-32542	90	6	set	set	NOUN
ajst-32542	90	7	is	be	AUX
ajst-32542	90	8	41.8155	41.8155	NUM
ajst-32542	90	9	,	,	PUNCT
ajst-32542	90	10	rmse	rmse	NOUN
ajst-32542	90	11	is	be	AUX
ajst-32542	90	12	46.9056	46.9056	NUM
ajst-32542	90	13	,	,	PUNCT
ajst-32542	90	14	and	and	CCONJ
ajst-32542	90	15	r²	r²	NOUN
ajst-32542	90	16	is	be	AUX
ajst-32542	90	17	0.33198	0.33198	NUM
ajst-32542	90	18	,	,	PUNCT
ajst-32542	90	19	while	while	SCONJ
ajst-32542	90	20	the	the	DET
ajst-32542	90	21	test	test	NOUN
ajst-32542	90	22	set	set	NOUN
ajst-32542	90	23	shows	show	VERB
ajst-32542	90	24	an	an	DET
ajst-32542	90	25	mae	mae	PROPN
ajst-32542	90	26	of	of	ADP
ajst-32542	90	27	31.7936	31.7936	NUM
ajst-32542	90	28	,	,	PUNCT
ajst-32542	90	29	rmse	rmse	NOUN
ajst-32542	90	30	of	of	ADP
ajst-32542	90	31	38.8063	38.8063	NUM
ajst-32542	90	32	,	,	PUNCT
ajst-32542	90	33	and	and	CCONJ
ajst-32542	90	34	r²	r²	VERB
ajst-32542	90	35	of	of	ADP
ajst-32542	90	36	0.79709	0.79709	NUM
ajst-32542	90	37	.	.	PUNCT
ajst-32542	91	1	this	this	PRON
ajst-32542	91	2	suggests	suggest	VERB
ajst-32542	91	3	that	that	SCONJ
ajst-32542	91	4	although	although	SCONJ
ajst-32542	91	5	the	the	DET
ajst-32542	91	6	model	model	NOUN
ajst-32542	91	7	fits	fit	VERB
ajst-32542	91	8	the	the	DET
ajst-32542	91	9	training	training	NOUN
ajst-32542	91	10	data	datum	NOUN
ajst-32542	91	11	well	well	ADV
ajst-32542	91	12	,	,	PUNCT
ajst-32542	91	13	it	it	PRON
ajst-32542	91	14	struggles	struggle	VERB
ajst-32542	91	15	with	with	ADP
ajst-32542	91	16	generalization	generalization	NOUN
ajst-32542	91	17	,	,	PUNCT
ajst-32542	91	18	especially	especially	ADV
ajst-32542	91	19	with	with	ADP
ajst-32542	91	20	significant	significant	ADJ
ajst-32542	91	21	prediction	prediction	NOUN
ajst-32542	91	22	errors	error	NOUN
ajst-32542	91	23	on	on	ADP
ajst-32542	91	24	the	the	DET
ajst-32542	91	25	validation	validation	NOUN
ajst-32542	91	26	set	set	NOUN
ajst-32542	91	27	.	.	PUNCT
ajst-32542	92	1	in	in	ADP
ajst-32542	92	2	contrast	contrast	NOUN
ajst-32542	92	3	,	,	PUNCT
ajst-32542	92	4	the	the	DET
ajst-32542	92	5	xgboost	xgboost	PROPN
ajst-32542	92	6	model	model	NOUN
ajst-32542	92	7	with	with	ADP
ajst-32542	92	8	ceemdan	ceemdan	PROPN
ajst-32542	92	9	shows	show	VERB
ajst-32542	92	10	improvements	improvement	NOUN
ajst-32542	92	11	in	in	ADP
ajst-32542	92	12	the	the	DET
ajst-32542	92	13	training	training	NOUN
ajst-32542	92	14	,	,	PUNCT
ajst-32542	92	15	validation	validation	NOUN
ajst-32542	92	16	,	,	PUNCT
ajst-32542	92	17	and	and	CCONJ
ajst-32542	92	18	test	test	NOUN
ajst-32542	92	19	sets	set	NOUN
ajst-32542	92	20	.	.	PUNCT
ajst-32542	93	1	the	the	DET
ajst-32542	93	2	mae	mae	PROPN
ajst-32542	93	3	for	for	ADP
ajst-32542	93	4	the	the	DET
ajst-32542	93	5	training	training	NOUN
ajst-32542	93	6	set	set	NOUN
ajst-32542	93	7	is	be	AUX
ajst-32542	93	8	7.1615	7.1615	NUM
ajst-32542	93	9	,	,	PUNCT
ajst-32542	93	10	mape	mape	NOUN
ajst-32542	93	11	is	be	AUX
ajst-32542	93	12	0.0053829	0.0053829	NUM
ajst-32542	93	13	,	,	PUNCT
ajst-32542	93	14	rmse	rmse	NOUN
ajst-32542	93	15	is	be	AUX
ajst-32542	93	16	9.6414	9.6414	NUM
ajst-32542	93	17	,	,	PUNCT
ajst-32542	93	18	and	and	CCONJ
ajst-32542	93	19	r²	r²	NOUN
ajst-32542	93	20	is	be	AUX
ajst-32542	93	21	0.99745	0.99745	NUM
ajst-32542	93	22	.	.	PUNCT
ajst-32542	94	1	although	although	SCONJ
ajst-32542	94	2	these	these	DET
ajst-32542	94	3	values	value	NOUN
ajst-32542	94	4	are	be	AUX
ajst-32542	94	5	slightly	slightly	ADV
ajst-32542	94	6	lower	low	ADJ
ajst-32542	94	7	than	than	ADP
ajst-32542	94	8	those	those	PRON
ajst-32542	94	9	of	of	ADP
ajst-32542	94	10	the	the	DET
ajst-32542	94	11	pure	pure	ADJ
ajst-32542	94	12	xgboost	xgboost	PROPN
ajst-32542	94	13	model	model	PROPN
ajst-32542	94	14	,	,	PUNCT
ajst-32542	94	15	the	the	DET
ajst-32542	94	16	prediction	prediction	NOUN
ajst-32542	94	17	accuracy	accuracy	NOUN
ajst-32542	94	18	remains	remain	VERB
ajst-32542	94	19	high	high	ADJ
ajst-32542	94	20	.	.	PUNCT
ajst-32542	95	1	on	on	ADP
ajst-32542	95	2	the	the	DET
ajst-32542	95	3	validation	validation	NOUN
ajst-32542	95	4	set	set	NOUN
ajst-32542	95	5	,	,	PUNCT
ajst-32542	95	6	ceemdan	ceemdan	PROPN
ajst-32542	95	7	-	-	PUNCT
ajst-32542	95	8	xgboost	xgboost	PROPN
ajst-32542	95	9	achieves	achieve	VERB
ajst-32542	95	10	a	a	DET
ajst-32542	95	11	mae	mae	PROPN
ajst-32542	95	12	of	of	ADP
ajst-32542	95	13	20.0696	20.0696	NUM
ajst-32542	95	14	,	,	PUNCT
ajst-32542	95	15	rmse	rmse	NOUN
ajst-32542	95	16	of	of	ADP
ajst-32542	95	17	26.0623	26.0623	NUM
ajst-32542	95	18	,	,	PUNCT
ajst-32542	95	19	and	and	CCONJ
ajst-32542	95	20	r²	r²	VERB
ajst-32542	95	21	of	of	ADP
ajst-32542	95	22	0.79376	0.79376	NUM
ajst-32542	95	23	,	,	PUNCT
ajst-32542	95	24	demonstrating	demonstrate	VERB
ajst-32542	95	25	more	more	ADV
ajst-32542	95	26	stable	stable	ADJ
ajst-32542	95	27	performance	performance	NOUN
ajst-32542	95	28	.	.	PUNCT
ajst-32542	96	1	the	the	DET
ajst-32542	96	2	test	test	NOUN
ajst-32542	96	3	set	set	NOUN
ajst-32542	96	4	shows	show	VERB
ajst-32542	96	5	a	a	DET
ajst-32542	96	6	mae	mae	PROPN
ajst-32542	96	7	of	of	ADP
ajst-32542	96	8	21.0753	21.0753	NUM
ajst-32542	96	9	,	,	PUNCT
ajst-32542	96	10	rmse	rmse	NOUN
ajst-32542	96	11	of	of	ADP
ajst-32542	96	12	26.4743	26.4743	NUM
ajst-32542	96	13	,	,	PUNCT
ajst-32542	96	14	and	and	CCONJ
ajst-32542	96	15	r²	r²	VERB
ajst-32542	96	16	of	of	ADP
ajst-32542	96	17	0.90556	0.90556	NUM
ajst-32542	96	18	,	,	PUNCT
ajst-32542	96	19	further	far	ADV
ajst-32542	96	20	confirming	confirm	VERB
ajst-32542	96	21	that	that	SCONJ
ajst-32542	96	22	this	this	DET
ajst-32542	96	23	model	model	NOUN
ajst-32542	96	24	significantly	significantly	ADV
ajst-32542	96	25	outperforms	outperform	VERB
ajst-32542	96	26	the	the	DET
ajst-32542	96	27	pure	pure	ADJ
ajst-32542	96	28	xgboost	xgboost	PROPN
ajst-32542	96	29	model	model	NOUN
ajst-32542	96	30	,	,	PUNCT
ajst-32542	96	31	especially	especially	ADV
ajst-32542	96	32	on	on	ADP
ajst-32542	96	33	the	the	DET
ajst-32542	96	34	test	test	NOUN
ajst-32542	96	35	set	set	NOUN
ajst-32542	96	36	.	.	PUNCT
ajst-32542	97	1	by	by	ADP
ajst-32542	97	2	comparing	compare	VERB
ajst-32542	97	3	the	the	DET
ajst-32542	97	4	results	result	NOUN
ajst-32542	97	5	of	of	ADP
ajst-32542	97	6	the	the	DET
ajst-32542	97	7	two	two	NUM
ajst-32542	97	8	models	model	NOUN
ajst-32542	97	9	,	,	PUNCT
ajst-32542	97	10	it	it	PRON
ajst-32542	97	11	is	be	AUX
ajst-32542	97	12	clear	clear	ADJ
ajst-32542	97	13	that	that	SCONJ
ajst-32542	97	14	introducing	introduce	VERB
ajst-32542	97	15	ceemdan	ceemdan	NOUN
ajst-32542	97	16	for	for	ADP
ajst-32542	97	17	data	datum	NOUN
ajst-32542	97	18	preprocessing	preprocessing	NOUN
ajst-32542	97	19	effectively	effectively	ADV
ajst-32542	97	20	improves	improve	VERB
ajst-32542	97	21	xgboost	xgboost	PROPN
ajst-32542	97	22	's	's	PART
ajst-32542	97	23	performance	performance	NOUN
ajst-32542	97	24	on	on	ADP
ajst-32542	97	25	both	both	CCONJ
ajst-32542	97	26	the	the	DET
ajst-32542	97	27	validation	validation	NOUN
ajst-32542	97	28	and	and	CCONJ
ajst-32542	97	29	test	test	NOUN
ajst-32542	97	30	sets	set	NOUN
ajst-32542	97	31	,	,	PUNCT
ajst-32542	97	32	particularly	particularly	ADV
ajst-32542	97	33	in	in	ADP
ajst-32542	97	34	terms	term	NOUN
ajst-32542	97	35	of	of	ADP
ajst-32542	97	36	prediction	prediction	NOUN
ajst-32542	97	37	accuracy	accuracy	NOUN
ajst-32542	97	38	and	and	CCONJ
ajst-32542	97	39	generalization	generalization	NOUN
ajst-32542	97	40	ability	ability	NOUN
ajst-32542	97	41	.	.	PUNCT
ajst-32542	98	1	ceemdan	ceemdan	ADJ
ajst-32542	98	2	extracts	extract	NOUN
ajst-32542	98	3	different	different	ADJ
ajst-32542	98	4	frequency	frequency	NOUN
ajst-32542	98	5	components	component	NOUN
ajst-32542	98	6	from	from	ADP
ajst-32542	98	7	the	the	DET
ajst-32542	98	8	signal	signal	NOUN
ajst-32542	98	9	,	,	PUNCT
ajst-32542	98	10	providing	provide	VERB
ajst-32542	98	11	more	more	ADV
ajst-32542	98	12	precise	precise	ADJ
ajst-32542	98	13	and	and	CCONJ
ajst-32542	98	14	meaningful	meaningful	ADJ
ajst-32542	98	15	features	feature	NOUN
ajst-32542	98	16	for	for	ADP
ajst-32542	98	17	xgboost	xgboost	PROPN
ajst-32542	98	18	,	,	PUNCT
ajst-32542	98	19	which	which	PRON
ajst-32542	98	20	in	in	ADP
ajst-32542	98	21	turn	turn	NOUN
ajst-32542	98	22	enhances	enhance	VERB
ajst-32542	98	23	the	the	DET
ajst-32542	98	24	overall	overall	ADJ
ajst-32542	98	25	performance	performance	NOUN
ajst-32542	98	26	of	of	ADP
ajst-32542	98	27	the	the	DET
ajst-32542	98	28	model	model	NOUN
ajst-32542	98	29	.	.	PUNCT
ajst-32542	99	1	94	94	NUM
ajst-32542	99	2	table	table	NOUN
ajst-32542	99	3	2	2	NUM
ajst-32542	99	4	.	.	PUNCT
ajst-32542	99	5	ablation	ablation	NOUN
ajst-32542	99	6	experiment	experiment	NOUN
ajst-32542	99	7	evaluation	evaluation	NOUN
ajst-32542	99	8	analysis	analysis	NOUN
ajst-32542	99	9	model	model	NOUN
ajst-32542	99	10	dataset	dataset	VERB
ajst-32542	99	11	mae	mae	PROPN
ajst-32542	99	12	mape	mape	PROPN
ajst-32542	99	13	mse	mse	PROPN
ajst-32542	99	14	rmse	rmse	PROPN
ajst-32542	99	15	r²	r²	VERB
ajst-32542	99	16	xgboost	xgboost	NOUN
ajst-32542	99	17	training	train	VERB
ajst-32542	99	18	3.6907	3.6907	NUM
ajst-32542	99	19	0.00277	0.00277	NUM
ajst-32542	99	20	22.5366	22.5366	NUM
ajst-32542	99	21	4.7473	4.7473	NUM
ajst-32542	99	22	0.99938	0.99938	NUM
ajst-32542	99	23	xgboost	xgboost	ADP
ajst-32542	99	24	validation	validation	NOUN
ajst-32542	99	25	41.8155	41.8155	NUM
ajst-32542	99	26	0.02327	0.02327	NUM
ajst-32542	100	1	2200.1346	2200.1346	NUM
ajst-32542	100	2	46.9056	46.9056	NUM
ajst-32542	100	3	0.33198	0.33198	NUM
ajst-32542	100	4	xgboost	xgboost	ADP
ajst-32542	100	5	test	test	NOUN
ajst-32542	100	6	31.7936	31.7936	NUM
ajst-32542	100	7	0.01769	0.01769	NUM
ajst-32542	100	8	1505.9255	1505.9255	NUM
ajst-32542	100	9	38.8063	38.8063	NUM
ajst-32542	100	10	0.79709	0.79709	NUM
ajst-32542	100	11	ceemdanxgboost	ceemdanxgboost	ADP
ajst-32542	100	12	training	train	VERB
ajst-32542	100	13	7.1615	7.1615	NUM
ajst-32542	100	14	0.00538	0.00538	NUM
ajst-32542	100	15	92.9563	92.9563	NUM
ajst-32542	100	16	9.6414	9.6414	NUM
ajst-32542	100	17	0.99745	0.99745	NUM
ajst-32542	100	18	ceemdanxgboost	ceemdanxgboost	ADP
ajst-32542	100	19	validation	validation	NOUN
ajst-32542	100	20	20.0696	20.0696	NUM
ajst-32542	100	21	0.01114	0.01114	NUM
ajst-32542	100	22	679.2455	679.2455	NUM
ajst-32542	100	23	26.0623	26.0623	NUM
ajst-32542	100	24	0.79376	0.79376	NUM
ajst-32542	100	25	ceemdanxgboost	ceemdanxgboost	ADP
ajst-32542	100	26	test	test	NOUN
ajst-32542	100	27	21.0753	21.0753	NUM
ajst-32542	100	28	0.01164	0.01164	NOUN
ajst-32542	100	29	700.8899	700.8899	NUM
ajst-32542	100	30	26.4743	26.4743	NUM
ajst-32542	100	31	0.90556	0.90556	NUM
ajst-32542	100	32	5	5	NUM
ajst-32542	100	33	.	.	PUNCT
ajst-32542	100	34	conclusion	conclusion	NOUN
ajst-32542	100	35	in	in	ADP
ajst-32542	100	36	this	this	DET
ajst-32542	100	37	study	study	NOUN
ajst-32542	100	38	,	,	PUNCT
ajst-32542	100	39	we	we	PRON
ajst-32542	100	40	proposed	propose	VERB
ajst-32542	100	41	a	a	DET
ajst-32542	100	42	hybrid	hybrid	ADJ
ajst-32542	100	43	modeling	modeling	NOUN
ajst-32542	100	44	method	method	NOUN
ajst-32542	100	45	combining	combine	VERB
ajst-32542	100	46	ceemdan	ceemdan	PROPN
ajst-32542	100	47	and	and	CCONJ
ajst-32542	100	48	xgboost	xgboost	NOUN
ajst-32542	100	49	to	to	PART
ajst-32542	100	50	address	address	VERB
ajst-32542	100	51	the	the	DET
ajst-32542	100	52	issues	issue	NOUN
ajst-32542	100	53	of	of	ADP
ajst-32542	100	54	nonlinearity	nonlinearity	NOUN
ajst-32542	100	55	and	and	CCONJ
ajst-32542	100	56	non	non	ADJ
ajst-32542	100	57	-	-	NOUN
ajst-32542	100	58	stationarity	stationarity	NOUN
ajst-32542	100	59	in	in	ADP
ajst-32542	100	60	gold	gold	NOUN
ajst-32542	100	61	price	price	NOUN
ajst-32542	100	62	prediction	prediction	NOUN
ajst-32542	100	63	.	.	PUNCT
ajst-32542	101	1	we	we	PRON
ajst-32542	101	2	first	first	ADV
ajst-32542	101	3	used	use	VERB
ajst-32542	101	4	ceemdan	ceemdan	PROPN
ajst-32542	101	5	to	to	PART
ajst-32542	101	6	decompose	decompose	VERB
ajst-32542	101	7	the	the	DET
ajst-32542	101	8	time	time	NOUN
ajst-32542	101	9	series	series	PROPN
ajst-32542	101	10	data	datum	NOUN
ajst-32542	101	11	of	of	ADP
ajst-32542	101	12	gold	gold	NOUN
ajst-32542	101	13	prices	price	NOUN
ajst-32542	101	14	,	,	PUNCT
ajst-32542	101	15	extracting	extract	VERB
ajst-32542	101	16	multiple	multiple	ADJ
ajst-32542	101	17	intrinsic	intrinsic	ADJ
ajst-32542	101	18	mode	mode	NOUN
ajst-32542	101	19	functions	function	NOUN
ajst-32542	101	20	(	(	PUNCT
ajst-32542	101	21	imfs	imfs	NOUN
ajst-32542	101	22	)	)	PUNCT
ajst-32542	101	23	,	,	PUNCT
ajst-32542	101	24	and	and	CCONJ
ajst-32542	101	25	then	then	ADV
ajst-32542	101	26	input	input	VERB
ajst-32542	101	27	these	these	DET
ajst-32542	101	28	decomposed	decompose	VERB
ajst-32542	101	29	components	component	NOUN
ajst-32542	101	30	as	as	ADP
ajst-32542	101	31	features	feature	NOUN
ajst-32542	101	32	into	into	ADP
ajst-32542	101	33	the	the	DET
ajst-32542	101	34	xgboost	xgboost	PROPN
ajst-32542	101	35	model	model	NOUN
ajst-32542	101	36	for	for	ADP
ajst-32542	101	37	training	training	NOUN
ajst-32542	101	38	and	and	CCONJ
ajst-32542	101	39	prediction	prediction	NOUN
ajst-32542	101	40	.	.	PUNCT
ajst-32542	102	1	by	by	ADP
ajst-32542	102	2	comparing	compare	VERB
ajst-32542	102	3	this	this	DET
ajst-32542	102	4	model	model	NOUN
ajst-32542	102	5	with	with	ADP
ajst-32542	102	6	the	the	DET
ajst-32542	102	7	traditional	traditional	ADJ
ajst-32542	102	8	xgboost	xgboost	NOUN
ajst-32542	102	9	,	,	PUNCT
ajst-32542	102	10	we	we	PRON
ajst-32542	102	11	demonstrated	demonstrate	VERB
ajst-32542	102	12	that	that	SCONJ
ajst-32542	102	13	the	the	DET
ajst-32542	102	14	introduction	introduction	NOUN
ajst-32542	102	15	of	of	ADP
ajst-32542	102	16	ceemdan	ceemdan	PROPN
ajst-32542	102	17	significantly	significantly	ADV
ajst-32542	102	18	improved	improve	VERB
ajst-32542	102	19	the	the	DET
ajst-32542	102	20	model	model	NOUN
ajst-32542	102	21	's	's	PART
ajst-32542	102	22	prediction	prediction	NOUN
ajst-32542	102	23	accuracy	accuracy	NOUN
ajst-32542	102	24	and	and	CCONJ
ajst-32542	102	25	generalization	generalization	NOUN
ajst-32542	102	26	ability	ability	NOUN
ajst-32542	102	27	.	.	PUNCT
ajst-32542	103	1	the	the	DET
ajst-32542	103	2	experimental	experimental	ADJ
ajst-32542	103	3	results	result	NOUN
ajst-32542	103	4	show	show	VERB
ajst-32542	103	5	that	that	SCONJ
ajst-32542	103	6	when	when	SCONJ
ajst-32542	103	7	xgboost	xgboost	X
ajst-32542	103	8	is	be	AUX
ajst-32542	103	9	used	use	VERB
ajst-32542	103	10	alone	alone	ADV
ajst-32542	103	11	,	,	PUNCT
ajst-32542	103	12	the	the	DET
ajst-32542	103	13	model	model	NOUN
ajst-32542	103	14	performs	perform	VERB
ajst-32542	103	15	excellently	excellently	ADV
ajst-32542	103	16	on	on	ADP
ajst-32542	103	17	the	the	DET
ajst-32542	103	18	training	training	NOUN
ajst-32542	103	19	set	set	NOUN
ajst-32542	103	20	but	but	CCONJ
ajst-32542	103	21	exhibits	exhibit	VERB
ajst-32542	103	22	considerable	considerable	ADJ
ajst-32542	103	23	prediction	prediction	NOUN
ajst-32542	103	24	errors	error	NOUN
ajst-32542	103	25	on	on	ADP
ajst-32542	103	26	the	the	DET
ajst-32542	103	27	validation	validation	NOUN
ajst-32542	103	28	and	and	CCONJ
ajst-32542	103	29	test	test	NOUN
ajst-32542	103	30	sets	set	NOUN
ajst-32542	103	31	.	.	PUNCT
ajst-32542	104	1	after	after	ADP
ajst-32542	104	2	introducing	introduce	VERB
ajst-32542	104	3	ceemdan	ceemdan	NOUN
ajst-32542	104	4	for	for	ADP
ajst-32542	104	5	data	datum	NOUN
ajst-32542	104	6	preprocessing	preprocessing	NOUN
ajst-32542	104	7	,	,	PUNCT
ajst-32542	104	8	xgboost	xgboost	PROPN
ajst-32542	104	9	's	's	PART
ajst-32542	104	10	performance	performance	NOUN
ajst-32542	104	11	on	on	ADP
ajst-32542	104	12	all	all	DET
ajst-32542	104	13	datasets	dataset	NOUN
ajst-32542	104	14	improved	improve	VERB
ajst-32542	104	15	notably	notably	ADV
ajst-32542	104	16	,	,	PUNCT
ajst-32542	104	17	especially	especially	ADV
ajst-32542	104	18	in	in	ADP
ajst-32542	104	19	terms	term	NOUN
ajst-32542	104	20	of	of	ADP
ajst-32542	104	21	prediction	prediction	NOUN
ajst-32542	104	22	accuracy	accuracy	NOUN
ajst-32542	104	23	and	and	CCONJ
ajst-32542	104	24	model	model	NOUN
ajst-32542	104	25	generalization	generalization	NOUN
ajst-32542	104	26	.	.	PUNCT
ajst-32542	105	1	specifically	specifically	ADV
ajst-32542	105	2	,	,	PUNCT
ajst-32542	105	3	the	the	DET
ajst-32542	105	4	ceemdan	ceemdan	PROPN
ajst-32542	105	5	+	+	CCONJ
ajst-32542	105	6	xgboost	xgboost	ADP
ajst-32542	105	7	model	model	NOUN
ajst-32542	105	8	not	not	PART
ajst-32542	105	9	only	only	ADV
ajst-32542	105	10	captures	capture	VERB
ajst-32542	105	11	the	the	DET
ajst-32542	105	12	dynamic	dynamic	ADJ
ajst-32542	105	13	trends	trend	NOUN
ajst-32542	105	14	of	of	ADP
ajst-32542	105	15	gold	gold	NOUN
ajst-32542	105	16	prices	price	NOUN
ajst-32542	105	17	more	more	ADV
ajst-32542	105	18	effectively	effectively	ADV
ajst-32542	105	19	but	but	CCONJ
ajst-32542	105	20	also	also	ADV
ajst-32542	105	21	enhances	enhance	VERB
ajst-32542	105	22	the	the	DET
ajst-32542	105	23	model	model	NOUN
ajst-32542	105	24	's	's	PART
ajst-32542	105	25	stability	stability	NOUN
ajst-32542	105	26	and	and	CCONJ
ajst-32542	105	27	predictive	predictive	ADJ
ajst-32542	105	28	ability	ability	NOUN
ajst-32542	105	29	on	on	ADP
ajst-32542	105	30	unseen	unseen	ADJ
ajst-32542	105	31	data	datum	NOUN
ajst-32542	105	32	.	.	PUNCT
ajst-32542	106	1	this	this	DET
ajst-32542	106	2	study	study	NOUN
ajst-32542	106	3	verifies	verify	VERB
ajst-32542	106	4	the	the	DET
ajst-32542	106	5	effectiveness	effectiveness	NOUN
ajst-32542	106	6	of	of	ADP
ajst-32542	106	7	ceemdan	ceemdan	PROPN
ajst-32542	106	8	in	in	ADP
ajst-32542	106	9	time	time	NOUN
ajst-32542	106	10	series	series	PROPN
ajst-32542	106	11	data	datum	NOUN
ajst-32542	106	12	processing	processing	NOUN
ajst-32542	106	13	and	and	CCONJ
ajst-32542	106	14	demonstrates	demonstrate	VERB
ajst-32542	106	15	that	that	SCONJ
ajst-32542	106	16	combining	combine	VERB
ajst-32542	106	17	it	it	PRON
ajst-32542	106	18	with	with	ADP
ajst-32542	106	19	xgboost	xgboost	ADV
ajst-32542	106	20	significantly	significantly	ADV
ajst-32542	106	21	boosts	boost	VERB
ajst-32542	106	22	the	the	DET
ajst-32542	106	23	model	model	NOUN
ajst-32542	106	24	's	's	PART
ajst-32542	106	25	prediction	prediction	NOUN
ajst-32542	106	26	performance	performance	NOUN
ajst-32542	106	27	.	.	PUNCT
ajst-32542	107	1	future	future	ADJ
ajst-32542	107	2	research	research	NOUN
ajst-32542	107	3	could	could	AUX
ajst-32542	107	4	explore	explore	VERB
ajst-32542	107	5	the	the	DET
ajst-32542	107	6	effects	effect	NOUN
ajst-32542	107	7	of	of	ADP
ajst-32542	107	8	different	different	ADJ
ajst-32542	107	9	signal	signal	NOUN
ajst-32542	107	10	decomposition	decomposition	NOUN
ajst-32542	107	11	methods	method	NOUN
ajst-32542	107	12	in	in	ADP
ajst-32542	107	13	combination	combination	NOUN
ajst-32542	107	14	with	with	ADP
ajst-32542	107	15	machine	machine	NOUN
ajst-32542	107	16	learning	learning	NOUN
ajst-32542	107	17	models	model	NOUN
ajst-32542	107	18	,	,	PUNCT
ajst-32542	107	19	as	as	ADV
ajst-32542	107	20	well	well	ADV
ajst-32542	107	21	as	as	ADP
ajst-32542	107	22	extend	extend	VERB
ajst-32542	107	23	this	this	DET
ajst-32542	107	24	approach	approach	NOUN
ajst-32542	107	25	to	to	ADP
ajst-32542	107	26	the	the	DET
ajst-32542	107	27	prediction	prediction	NOUN
ajst-32542	107	28	of	of	ADP
ajst-32542	107	29	other	other	ADJ
ajst-32542	107	30	non	non	ADJ
ajst-32542	107	31	-	-	ADJ
ajst-32542	107	32	stationary	stationary	ADJ
ajst-32542	107	33	time	time	NOUN
ajst-32542	107	34	series	series	PROPN
ajst-32542	107	35	data	data	PROPN
ajst-32542	107	36	,	,	PUNCT
ajst-32542	107	37	enhancing	enhance	VERB
ajst-32542	107	38	its	its	PRON
ajst-32542	107	39	application	application	NOUN
ajst-32542	107	40	potential	potential	NOUN
ajst-32542	107	41	in	in	ADP
ajst-32542	107	42	fields	field	NOUN
ajst-32542	107	43	such	such	ADJ
ajst-32542	107	44	as	as	ADP
ajst-32542	107	45	finance	finance	NOUN
ajst-32542	107	46	,	,	PUNCT
ajst-32542	107	47	energy	energy	NOUN
ajst-32542	107	48	,	,	PUNCT
ajst-32542	107	49	and	and	CCONJ
ajst-32542	107	50	meteorology	meteorology	NOUN
ajst-32542	107	51	.	.	PUNCT
ajst-32542	108	1	references	reference	NOUN
ajst-32542	108	2	[	[	X
ajst-32542	108	3	1	1	X
ajst-32542	108	4	]	]	PUNCT
ajst-32542	108	5	s.	s.	PROPN
ajst-32542	108	6	chandar	chandar	PROPN
ajst-32542	108	7	,	,	PUNCT
ajst-32542	108	8	s.	s.	PROPN
ajst-32542	108	9	mahendran	mahendran	PROPN
ajst-32542	108	10	,	,	PUNCT
ajst-32542	108	11	and	and	CCONJ
ajst-32542	108	12	s.	s.	PROPN
ajst-32542	108	13	natarajan	natarajan	PROPN
ajst-32542	108	14	,	,	PUNCT
ajst-32542	108	15	“	"	PUNCT
ajst-32542	108	16	forecasting	forecast	VERB
ajst-32542	108	17	gold	gold	NOUN
ajst-32542	108	18	prices	price	NOUN
ajst-32542	108	19	based	base	VERB
ajst-32542	108	20	on	on	ADP
ajst-32542	108	21	extreme	extreme	ADJ
ajst-32542	108	22	learning	learning	NOUN
ajst-32542	108	23	machine	machine	NOUN
ajst-32542	108	24	,	,	PUNCT
ajst-32542	108	25	”	"	PUNCT
ajst-32542	108	26	int	int	NOUN
ajst-32542	108	27	.	.	PUNCT
ajst-32542	109	1	j.	j.	PROPN
ajst-32542	109	2	comput	comput	PROPN
ajst-32542	109	3	.	.	PUNCT
ajst-32542	110	1	commun	commun	PROPN
ajst-32542	110	2	.	.	PUNCT
ajst-32542	111	1	control	control	PROPN
ajst-32542	111	2	,	,	PUNCT
ajst-32542	111	3	vol	vol	NOUN
ajst-32542	111	4	.	.	PROPN
ajst-32542	111	5	11	11	NUM
ajst-32542	111	6	,	,	PUNCT
ajst-32542	111	7	pp	pp	ADJ
ajst-32542	111	8	.	.	PUNCT
ajst-32542	112	1	372–380	372–380	NUM
ajst-32542	112	2	,	,	PUNCT
ajst-32542	112	3	2016	2016	NUM
ajst-32542	112	4	.	.	PUNCT
ajst-32542	113	1	[	[	X
ajst-32542	113	2	2	2	NUM
ajst-32542	113	3	]	]	PUNCT
ajst-32542	113	4	r.	r.	PROPN
ajst-32542	113	5	kumar	kumar	PROPN
ajst-32542	113	6	,	,	PUNCT
ajst-32542	113	7	j.	j.	PROPN
ajst-32542	113	8	moolchandani	moolchandani	PROPN
ajst-32542	113	9	,	,	PUNCT
ajst-32542	113	10	a.	a.	NOUN
ajst-32542	113	11	shukla	shukla	NOUN
ajst-32542	113	12	,	,	PUNCT
ajst-32542	113	13	s.	s.	PROPN
ajst-32542	113	14	sahu	sahu	PROPN
ajst-32542	113	15	,	,	PUNCT
ajst-32542	113	16	v.	v.	PROPN
ajst-32542	113	17	thada	thada	PROPN
ajst-32542	113	18	,	,	PUNCT
ajst-32542	113	19	and	and	CCONJ
ajst-32542	113	20	v.	v.	ADP
ajst-32542	113	21	chole	chole	NOUN
ajst-32542	113	22	,	,	PUNCT
ajst-32542	113	23	“	"	PUNCT
ajst-32542	113	24	machine	machine	NOUN
ajst-32542	113	25	learning	learning	NOUN
ajst-32542	113	26	-	-	PUNCT
ajst-32542	113	27	based	base	VERB
ajst-32542	113	28	prediction	prediction	NOUN
ajst-32542	113	29	of	of	ADP
ajst-32542	113	30	gold	gold	NOUN
ajst-32542	113	31	prices	price	NOUN
ajst-32542	113	32	using	use	VERB
ajst-32542	113	33	economic	economic	ADJ
ajst-32542	113	34	indicators	indicator	NOUN
ajst-32542	113	35	,	,	PUNCT
ajst-32542	113	36	”	"	PUNCT
ajst-32542	113	37	in	in	ADP
ajst-32542	113	38	proc	proc	NOUN
ajst-32542	113	39	.	.	PUNCT
ajst-32542	113	40	13th	13th	ADJ
ajst-32542	113	41	int	int	NOUN
ajst-32542	113	42	.	.	PUNCT
ajst-32542	114	1	conf	conf	NOUN
ajst-32542	114	2	.	.	PUNCT
ajst-32542	115	1	system	system	NOUN
ajst-32542	115	2	modeling	modeling	NOUN
ajst-32542	115	3	&	&	CCONJ
ajst-32542	115	4	advancement	advancement	NOUN
ajst-32542	115	5	in	in	ADP
ajst-32542	115	6	research	research	NOUN
ajst-32542	115	7	trends	trend	NOUN
ajst-32542	115	8	(	(	PUNCT
ajst-32542	115	9	smart	smart	ADJ
ajst-32542	115	10	)	)	PUNCT
ajst-32542	115	11	,	,	PUNCT
ajst-32542	115	12	2024	2024	NUM
ajst-32542	115	13	,	,	PUNCT
ajst-32542	115	14	pp	pp	ADP
ajst-32542	115	15	.	.	PUNCT
ajst-32542	116	1	520–524	520–524	NUM
ajst-32542	116	2	.	.	PUNCT
ajst-32542	117	1	[	[	X
ajst-32542	117	2	3	3	X
ajst-32542	117	3	]	]	X
ajst-32542	117	4	c.	c.	PROPN
ajst-32542	117	5	qiu	qiu	PROPN
ajst-32542	117	6	et	et	PROPN
ajst-32542	117	7	al	al	PROPN
ajst-32542	117	8	.	.	PROPN
ajst-32542	117	9	,	,	PUNCT
ajst-32542	117	10	“	"	PUNCT
ajst-32542	117	11	a	a	DET
ajst-32542	117	12	two	two	NUM
ajst-32542	117	13	-	-	PUNCT
ajst-32542	117	14	stage	stage	NOUN
ajst-32542	117	15	deep	deep	ADJ
ajst-32542	117	16	fusion	fusion	NOUN
ajst-32542	117	17	integration	integration	NOUN
ajst-32542	117	18	framework	framework	NOUN
ajst-32542	117	19	based	base	VERB
ajst-32542	117	20	on	on	ADP
ajst-32542	117	21	feature	feature	NOUN
ajst-32542	117	22	fusion	fusion	NOUN
ajst-32542	117	23	and	and	CCONJ
ajst-32542	117	24	residual	residual	ADJ
ajst-32542	117	25	correction	correction	NOUN
ajst-32542	117	26	for	for	ADP
ajst-32542	117	27	gold	gold	NOUN
ajst-32542	117	28	price	price	NOUN
ajst-32542	117	29	forecasting	forecasting	NOUN
ajst-32542	117	30	,	,	PUNCT
ajst-32542	117	31	”	"	PUNCT
ajst-32542	117	32	ieee	ieee	NOUN
ajst-32542	117	33	access	access	NOUN
ajst-32542	117	34	,	,	PUNCT
ajst-32542	117	35	vol	vol	NOUN
ajst-32542	117	36	.	.	PROPN
ajst-32542	117	37	12	12	NUM
ajst-32542	117	38	,	,	PUNCT
ajst-32542	117	39	pp	pp	PROPN
ajst-32542	117	40	.	.	PUNCT
ajst-32542	117	41	85565–85579	85565–85579	NUM
ajst-32542	117	42	,	,	PUNCT
ajst-32542	117	43	2024	2024	NUM
ajst-32542	117	44	.	.	PUNCT
ajst-32542	118	1	[	[	X
ajst-32542	118	2	4	4	NUM
ajst-32542	118	3	]	]	PUNCT
ajst-32542	118	4	a.	a.	NOUN
ajst-32542	118	5	gadhave	gadhave	PROPN
ajst-32542	118	6	,	,	PUNCT
ajst-32542	118	7	“	"	PUNCT
ajst-32542	118	8	gold	gold	NOUN
ajst-32542	118	9	price	price	NOUN
ajst-32542	118	10	prediction	prediction	NOUN
ajst-32542	118	11	using	use	VERB
ajst-32542	118	12	machine	machine	NOUN
ajst-32542	118	13	learning	learning	NOUN
ajst-32542	118	14	,	,	PUNCT
ajst-32542	118	15	”	"	PUNCT
ajst-32542	118	16	int	int	NOUN
ajst-32542	118	17	.	.	PUNCT
ajst-32542	119	1	j.	j.	PROPN
ajst-32542	119	2	sci	sci	PROPN
ajst-32542	119	3	.	.	PUNCT
ajst-32542	120	1	res	res	PROPN
ajst-32542	120	2	.	.	PUNCT
ajst-32542	121	1	eng	eng	PROPN
ajst-32542	121	2	.	.	PROPN
ajst-32542	122	1	manag	manag	PROPN
ajst-32542	122	2	.	.	PROPN
ajst-32542	122	3	,	,	PUNCT
ajst-32542	122	4	2022	2022	NUM
ajst-32542	122	5	.	.	PUNCT
ajst-32542	123	1	[	[	X
ajst-32542	123	2	5	5	X
ajst-32542	123	3	]	]	PUNCT
ajst-32542	123	4	s.	s.	PROPN
ajst-32542	123	5	liu	liu	PROPN
ajst-32542	123	6	sentiko	sentiko	PROPN
ajst-32542	123	7	,	,	PUNCT
ajst-32542	123	8	a.	a.	PROPN
ajst-32542	123	9	y.	y.	PROPN
ajst-32542	123	10	zakiyyah	zakiyyah	PROPN
ajst-32542	123	11	,	,	PUNCT
ajst-32542	123	12	and	and	CCONJ
ajst-32542	123	13	meiliana	meiliana	ADJ
ajst-32542	123	14	,	,	PUNCT
ajst-32542	123	15	“	"	PUNCT
ajst-32542	123	16	gold	gold	ADJ
ajst-32542	123	17	price	price	NOUN
ajst-32542	123	18	prediction	prediction	NOUN
ajst-32542	123	19	using	use	VERB
ajst-32542	123	20	machine	machine	NOUN
ajst-32542	123	21	learning	learning	NOUN
ajst-32542	123	22	and	and	CCONJ
ajst-32542	123	23	deep	deep	ADJ
ajst-32542	123	24	learning	learning	NOUN
ajst-32542	123	25	,	,	PUNCT
ajst-32542	123	26	”	"	PUNCT
ajst-32542	123	27	in	in	ADP
ajst-32542	123	28	proc	proc	NOUN
ajst-32542	123	29	.	.	PUNCT
ajst-32542	124	1	6th	6th	ADJ
ajst-32542	124	2	int	int	PROPN
ajst-32542	124	3	.	.	PUNCT
ajst-32542	124	4	conf	conf	NOUN
ajst-32542	124	5	.	.	PUNCT
ajst-32542	125	1	cybernetics	cybernetic	NOUN
ajst-32542	125	2	and	and	CCONJ
ajst-32542	125	3	intelligent	intelligent	ADJ
ajst-32542	125	4	system	system	NOUN
ajst-32542	125	5	(	(	PUNCT
ajst-32542	125	6	icoris	icoris	NOUN
ajst-32542	125	7	)	)	PUNCT
ajst-32542	125	8	,	,	PUNCT
ajst-32542	125	9	2024	2024	NUM
ajst-32542	125	10	.	.	PUNCT
ajst-32542	126	1	[	[	X
ajst-32542	126	2	6	6	NUM
ajst-32542	126	3	]	]	X
ajst-32542	126	4	y.	y.	PROPN
ajst-32542	126	5	wang	wang	PROPN
ajst-32542	126	6	and	and	CCONJ
ajst-32542	126	7	t.	t.	PROPN
ajst-32542	126	8	lin	lin	PROPN
ajst-32542	126	9	,	,	PUNCT
ajst-32542	126	10	“	"	PUNCT
ajst-32542	126	11	a	a	DET
ajst-32542	126	12	novel	novel	ADJ
ajst-32542	126	13	deterministic	deterministic	ADJ
ajst-32542	126	14	probabilistic	probabilistic	ADJ
ajst-32542	126	15	forecasting	forecasting	NOUN
ajst-32542	126	16	framework	framework	NOUN
ajst-32542	126	17	for	for	ADP
ajst-32542	126	18	gold	gold	NOUN
ajst-32542	126	19	price	price	NOUN
ajst-32542	126	20	with	with	ADP
ajst-32542	126	21	a	a	DET
ajst-32542	126	22	new	new	ADJ
ajst-32542	126	23	pandemic	pandemic	ADJ
ajst-32542	126	24	index	index	NOUN
ajst-32542	126	25	,	,	PUNCT
ajst-32542	126	26	”	"	PUNCT
ajst-32542	126	27	mathematics	mathematic	NOUN
ajst-32542	126	28	,	,	PUNCT
ajst-32542	126	29	2023	2023	NUM
ajst-32542	126	30	.	.	PUNCT
ajst-32542	127	1	[	[	X
ajst-32542	127	2	7	7	X
ajst-32542	127	3	]	]	X
ajst-32542	127	4	n.	n.	NOUN
ajst-32542	127	5	m.	m.	NOUN
ajst-32542	127	6	trieu	trieu	PROPN
ajst-32542	127	7	and	and	CCONJ
ajst-32542	127	8	n.	n.	PROPN
ajst-32542	127	9	t.	t.	PROPN
ajst-32542	127	10	thinh	thinh	PROPN
ajst-32542	127	11	,	,	PUNCT
ajst-32542	127	12	“	"	PUNCT
ajst-32542	127	13	a	a	DET
ajst-32542	127	14	robust	robust	ADJ
ajst-32542	127	15	prediction	prediction	NOUN
ajst-32542	127	16	for	for	ADP
ajst-32542	127	17	gold	gold	NOUN
ajst-32542	127	18	price	price	NOUN
ajst-32542	127	19	using	use	VERB
ajst-32542	127	20	heterogeneous	heterogeneous	ADJ
ajst-32542	127	21	ensemble	ensemble	ADJ
ajst-32542	127	22	learning	learning	NOUN
ajst-32542	127	23	,	,	PUNCT
ajst-32542	127	24	”	"	PUNCT
ajst-32542	127	25	in	in	ADP
ajst-32542	127	26	proc	proc	NOUN
ajst-32542	127	27	.	.	PUNCT
ajst-32542	128	1	13th	13th	ADJ
ajst-32542	128	2	int	int	NOUN
ajst-32542	128	3	.	.	PUNCT
ajst-32542	129	1	conf	conf	NOUN
ajst-32542	129	2	.	.	PUNCT
ajst-32542	130	1	control	control	PROPN
ajst-32542	130	2	,	,	PUNCT
ajst-32542	130	3	automation	automation	NOUN
ajst-32542	130	4	and	and	CCONJ
ajst-32542	130	5	information	information	NOUN
ajst-32542	130	6	sciences	sciences	PROPN
ajst-32542	130	7	(	(	PUNCT
ajst-32542	130	8	iccais	iccais	PROPN
ajst-32542	130	9	)	)	PUNCT
ajst-32542	130	10	,	,	PUNCT
ajst-32542	130	11	2024	2024	NUM
ajst-32542	130	12	,	,	PUNCT
ajst-32542	130	13	pp	pp	ADV
ajst-32542	130	14	.	.	PUNCT
ajst-32542	131	1	1–5	1–5	X
ajst-32542	131	2	.	.	PUNCT
ajst-32542	132	1	[	[	X
ajst-32542	132	2	8	8	NUM
ajst-32542	132	3	]	]	X
ajst-32542	132	4	w.	w.	PROPN
ajst-32542	132	5	gong	gong	PROPN
ajst-32542	132	6	,	,	PUNCT
ajst-32542	132	7	“	"	PUNCT
ajst-32542	132	8	research	research	NOUN
ajst-32542	132	9	on	on	ADP
ajst-32542	132	10	gold	gold	NOUN
ajst-32542	132	11	price	price	NOUN
ajst-32542	132	12	forecasting	forecasting	NOUN
ajst-32542	132	13	based	base	VERB
ajst-32542	132	14	on	on	ADP
ajst-32542	132	15	lstm	lstm	NOUN
ajst-32542	132	16	and	and	CCONJ
ajst-32542	132	17	linear	linear	PROPN
ajst-32542	132	18	regression	regression	NOUN
ajst-32542	132	19	,	,	PUNCT
ajst-32542	132	20	”	"	PUNCT
ajst-32542	132	21	shs	shs	PROPN
ajst-32542	132	22	web	web	NOUN
ajst-32542	132	23	conf	conf	NOUN
ajst-32542	132	24	.	.	PUNCT
ajst-32542	132	25	,	,	PUNCT
ajst-32542	132	26	2024	2024	NUM
ajst-32542	132	27	.	.	PUNCT
ajst-32542	132	28	95	95	NUM
ajst-32542	133	1	[	[	X
ajst-32542	133	2	9	9	NUM
ajst-32542	133	3	]	]	X
ajst-32542	133	4	d.	d.	PROPN
ajst-32542	133	5	m.	m.	PROPN
ajst-32542	133	6	t.	t.	PROPN
ajst-32542	133	7	nguyen	nguyen	PROPN
ajst-32542	133	8	,	,	PUNCT
ajst-32542	133	9	n.	n.	PROPN
ajst-32542	133	10	c.	c.	PROPN
ajst-32542	133	11	debnath	debnath	PROPN
ajst-32542	133	12	,	,	PUNCT
ajst-32542	133	13	l.-d	l.-d	PROPN
ajst-32542	133	14	.	.	PUNCT
ajst-32542	133	15	quach	quach	NOUN
ajst-32542	133	16	,	,	PUNCT
ajst-32542	133	17	and	and	CCONJ
ajst-32542	133	18	v.	v.	ADP
ajst-32542	133	19	d.	d.	PROPN
ajst-32542	133	20	nguyen	nguyen	PROPN
ajst-32542	133	21	,	,	PUNCT
ajst-32542	133	22	“	"	PUNCT
ajst-32542	133	23	machine	machine	NOUN
ajst-32542	133	24	learning	learn	VERB
ajst-32542	133	25	algorithms	algorithm	NOUN
ajst-32542	133	26	for	for	ADP
ajst-32542	133	27	gold	gold	NOUN
ajst-32542	133	28	price	price	NOUN
ajst-32542	133	29	prediction	prediction	NOUN
ajst-32542	133	30	,	,	PUNCT
ajst-32542	133	31	”	"	PUNCT
ajst-32542	133	32	in	in	ADP
ajst-32542	133	33	proc	proc	NOUN
ajst-32542	133	34	.	.	PUNCT
ajst-32542	134	1	int	int	NOUN
ajst-32542	134	2	.	.	PUNCT
ajst-32542	134	3	conf	conf	PROPN
ajst-32542	134	4	.	.	PUNCT
ajst-32542	135	1	advances	advance	NOUN
ajst-32542	135	2	in	in	ADP
ajst-32542	135	3	information	information	NOUN
ajst-32542	135	4	technology	technology	NOUN
ajst-32542	135	5	and	and	CCONJ
ajst-32542	135	6	education	education	NOUN
ajst-32542	135	7	,	,	PUNCT
ajst-32542	135	8	2023	2023	NUM
ajst-32542	135	9	,	,	PUNCT
ajst-32542	135	10	pp	pp	ADJ
ajst-32542	135	11	.	.	PUNCT
ajst-32542	136	1	212–220	212–220	NUM
ajst-32542	136	2	.	.	PUNCT
ajst-32542	137	1	[	[	X
ajst-32542	137	2	10	10	NUM
ajst-32542	137	3	]	]	X
ajst-32542	137	4	h.	h.	PROPN
ajst-32542	137	5	zangana	zangana	PROPN
ajst-32542	137	6	and	and	CCONJ
ajst-32542	137	7	s.	s.	PROPN
ajst-32542	137	8	r.	r.	PROPN
ajst-32542	137	9	obeyd	obeyd	PROPN
ajst-32542	137	10	,	,	PUNCT
ajst-32542	137	11	“	"	PUNCT
ajst-32542	137	12	deep	deep	ADJ
ajst-32542	137	13	learning	learning	NOUN
ajst-32542	137	14	-	-	PUNCT
ajst-32542	137	15	based	base	VERB
ajst-32542	137	16	gold	gold	NOUN
ajst-32542	137	17	price	price	NOUN
ajst-32542	137	18	prediction	prediction	NOUN
ajst-32542	137	19	:	:	PUNCT
ajst-32542	137	20	a	a	DET
ajst-32542	137	21	novel	novel	ADJ
ajst-32542	137	22	approach	approach	NOUN
ajst-32542	137	23	using	use	VERB
ajst-32542	137	24	time	time	NOUN
ajst-32542	137	25	series	series	PROPN
ajst-32542	137	26	analysis	analysis	NOUN
ajst-32542	137	27	,	,	PUNCT
ajst-32542	137	28	”	"	PUNCT
ajst-32542	137	29	sistemasi	sistemasi	NOUN
ajst-32542	137	30	,	,	PUNCT
ajst-32542	137	31	2024	2024	NUM
ajst-32542	137	32	.	.	PUNCT
ajst-32542	138	1	[	[	X
ajst-32542	138	2	11	11	NUM
ajst-32542	138	3	]	]	PUNCT
ajst-32542	138	4	s.	s.	PROPN
ajst-32542	138	5	duman	duman	PROPN
ajst-32542	138	6	,	,	PUNCT
ajst-32542	138	7	s.	s.	PROPN
ajst-32542	138	8	turnacıgil	turnacıgil	PROPN
ajst-32542	138	9	,	,	PUNCT
ajst-32542	138	10	e.	e.	PROPN
ajst-32542	138	11	arık	arık	PROPN
ajst-32542	138	12	,	,	PUNCT
ajst-32542	138	13	and	and	CCONJ
ajst-32542	138	14	m.	m.	NOUN
ajst-32542	138	15	a.	a.	NOUN
ajst-32542	138	16	aktaş	aktaş	PROPN
ajst-32542	138	17	,	,	PUNCT
ajst-32542	138	18	“	"	PUNCT
ajst-32542	138	19	the	the	DET
ajst-32542	138	20	role	role	NOUN
ajst-32542	138	21	of	of	ADP
ajst-32542	138	22	international	international	ADJ
ajst-32542	138	23	variables	variable	NOUN
ajst-32542	138	24	in	in	ADP
ajst-32542	138	25	predicting	predict	VERB
ajst-32542	138	26	gold	gold	ADJ
ajst-32542	138	27	prices	price	NOUN
ajst-32542	138	28	:	:	PUNCT
ajst-32542	138	29	analysis	analysis	NOUN
ajst-32542	138	30	with	with	ADP
ajst-32542	138	31	machine	machine	NOUN
ajst-32542	138	32	learning	learning	NOUN
ajst-32542	138	33	algorithms	algorithm	NOUN
ajst-32542	138	34	,	,	PUNCT
ajst-32542	138	35	”	"	PUNCT
ajst-32542	138	36	sosyoekonomi	sosyoekonomi	NOUN
ajst-32542	138	37	,	,	PUNCT
ajst-32542	138	38	2024	2024	NUM
ajst-32542	138	39	.	.	PUNCT