{"id":"https://openalex.org/W4283765503","doi":"https://doi.org/10.1186/s43067-022-00054-1","title":"Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis","display_name":"Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis","publication_year":2022,"publication_date":"2022-06-30","ids":{"openalex":"https://openalex.org/W4283765503","doi":"https://doi.org/10.1186/s43067-022-00054-1"},"language":"en","primary_location":{"id":"doi:10.1186/s43067-022-00054-1","doi":"https://doi.org/10.1186/s43067-022-00054-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s43067-022-00054-1","pdf_url":"https://jesit.springeropen.com/counter/pdf/10.1186/s43067-022-00054-1","source":{"id":"https://openalex.org/S2501379583","display_name":"Journal of Electrical Systems and Information Technology","issn_l":"2314-7172","issn":["2314-7172"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1"],"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Electrical Systems and Information Technology","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://jesit.springeropen.com/counter/pdf/10.1186/s43067-022-00054-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070540750","display_name":"Michael Ayitey","orcid":"https://orcid.org/0000-0002-6573-1977","observed_orcids":["https://orcid.org/0000-0002-6573-1977"]},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":true,"raw_author_name":"Michael Ayitey Junior","raw_affiliation_strings":["Department of Computer Science and Informatics, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana"],"raw_orcid":"https://orcid.org/0000-0002-6573-1977","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Informatics, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089844476","display_name":"Peter Appiahene","orcid":"https://orcid.org/0000-0002-6098-4537","observed_orcids":["https://orcid.org/0000-0002-6098-4537"]},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":false,"raw_author_name":"Peter Appiahene","raw_affiliation_strings":["Department of Computer Science and Informatics, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Informatics, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007815734","display_name":"Obed Appiah","orcid":"https://orcid.org/0000-0002-4680-3015","observed_orcids":["https://orcid.org/0000-0002-4680-3015","https://orcid.org/0009-0009-9686-5730","https://orcid.org/0009-0000-9841-230X"]},"institutions":[{"id":"https://openalex.org/I291863076","display_name":"University of Energy and Natural Resources","ror":"https://ror.org/05r9rzb75","country_code":"GH","type":"education","lineage":["https://openalex.org/I291863076"]}],"countries":["GH"],"is_corresponding":false,"raw_author_name":"Obed Appiah","raw_affiliation_strings":["Department of Computer Science and Informatics, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Informatics, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana","institution_ids":["https://openalex.org/I291863076"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5070540750"],"corresponding_institution_ids":["https://openalex.org/I291863076"],"apc_list":{"value":0,"currency":"USD","value_usd":0},"apc_paid":null,"fwci":3.6734,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.93363201,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"9","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stacked-lstm","display_name":"stacked LSTM","score":0.7289999723434448},{"id":"https://openalex.org/keywords/long-short-term-memory-neural-network","display_name":"long short-term memory neural network","score":0.7009999752044678},{"id":"https://openalex.org/keywords/hurst-exponent","display_name":"Hurst exponent","score":0.9789999723434448},{"id":"https://openalex.org/keywords/time-series-predictability","display_name":"time series predictability","score":0.6919999718666077},{"id":"https://openalex.org/keywords/audusd","display_name":"AUDUSD","score":0.8619999885559082},{"id":"https://openalex.org/keywords/currency-correlation","display_name":"currency correlation","score":0.4790000021457672},{"id":"https://openalex.org/keywords/trading-decision-support","display_name":"trading decision support","score":0.47999998927116394}],"concepts":[{"id":"https://openalex.org/C536366893","wikidata":"https://www.wikidata.org/wiki/Q66076","display_name":"Foreign exchange market","level":3,"score":0.8379786610603333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6248322129249573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5704752802848816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5564631223678589},{"id":"https://openalex.org/C96835011","wikidata":"https://www.wikidata.org/wiki/Q1638718","display_name":"Hurst exponent","level":2,"score":0.5265781283378601},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4659005105495453},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41711336374282837},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.41335296630859375},{"id":"https://openalex.org/C141121606","wikidata":"https://www.wikidata.org/wiki/Q8142","display_name":"Currency","level":2,"score":0.36979857087135315},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32416874170303345},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2783646583557129},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15008997917175293},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14092785120010376},{"id":"https://openalex.org/C556758197","wikidata":"https://www.wikidata.org/wiki/Q580018","display_name":"Monetary economics","level":1,"score":0.13941705226898193}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s43067-022-00054-1","doi":"https://doi.org/10.1186/s43067-022-00054-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s43067-022-00054-1","pdf_url":"https://jesit.springeropen.com/counter/pdf/10.1186/s43067-022-00054-1","source":{"id":"https://openalex.org/S2501379583","display_name":"Journal of Electrical Systems and Information Technology","issn_l":"2314-7172","issn":["2314-7172"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1"],"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Electrical Systems and Information Technology","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:11995e80a2fd4f8e80cb371e0d9cfd3d","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/11995e80a2fd4f8e80cb371e0d9cfd3d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Electrical Systems and Information Technology, Vol 9, Iss 1, Pp 1-24 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s43067-022-00054-1","doi":"https://doi.org/10.1186/s43067-022-00054-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s43067-022-00054-1","pdf_url":"https://jesit.springeropen.com/counter/pdf/10.1186/s43067-022-00054-1","source":{"id":"https://openalex.org/S2501379583","display_name":"Journal of Electrical Systems and Information Technology","issn_l":"2314-7172","issn":["2314-7172"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1"],"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Electrical Systems and Information Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283765503.pdf","grobid_xml":"https://content.openalex.org/works/W4283765503.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1983479840","https://openalex.org/W1987429725","https://openalex.org/W2063706130","https://openalex.org/W2076034298","https://openalex.org/W2097681719","https://openalex.org/W2113952909","https://openalex.org/W2119002620","https://openalex.org/W2121317260","https://openalex.org/W2123753389","https://openalex.org/W2142218983","https://openalex.org/W2268677264","https://openalex.org/W2333233800","https://openalex.org/W2477674601","https://openalex.org/W2522623928","https://openalex.org/W2565522229","https://openalex.org/W2594444301","https://openalex.org/W2594839142","https://openalex.org/W2597372336","https://openalex.org/W2728073923","https://openalex.org/W2750268091","https://openalex.org/W2802313218","https://openalex.org/W2802785059","https://openalex.org/W2884603773","https://openalex.org/W2906565052","https://openalex.org/W2909342716","https://openalex.org/W2913797286","https://openalex.org/W2921917752","https://openalex.org/W2958786696","https://openalex.org/W2982109826","https://openalex.org/W2995227017","https://openalex.org/W2996485148","https://openalex.org/W2997549107","https://openalex.org/W2999124956","https://openalex.org/W2999145386","https://openalex.org/W3000095666","https://openalex.org/W3006047666","https://openalex.org/W3007001424","https://openalex.org/W3008000006","https://openalex.org/W3010718760","https://openalex.org/W3012955798","https://openalex.org/W3015163914","https://openalex.org/W3015898022","https://openalex.org/W3029872365","https://openalex.org/W3080346373","https://openalex.org/W3083701701","https://openalex.org/W3094452610","https://openalex.org/W3095388897","https://openalex.org/W3130713566","https://openalex.org/W3130860156","https://openalex.org/W3138464002","https://openalex.org/W3142379219","https://openalex.org/W3151864675","https://openalex.org/W3156531028","https://openalex.org/W3174944207","https://openalex.org/W3186720625"],"related_works":["https://openalex.org/W2995227017","https://openalex.org/W7019270589","https://openalex.org/W3015163914","https://openalex.org/W2952531941","https://openalex.org/W7028511089","https://openalex.org/W4409360491","https://openalex.org/W4387042906","https://openalex.org/W7171784054","https://openalex.org/W2276106517","https://openalex.org/W3200576881"],"abstract_inverted_index":{"Abstract":[0],"Since":[1],"it":[2],"is":[3,43,113,128,141,171,222,322],"one":[4],"of":[5,22,47,56,64,91,99,117,168,210,228,232,332,372],"the":[6,12,26,57,83,97,137,157,177,193,208,211,226,246,248,251,256,260,301,316,325,370,373],"world's":[7],"most":[8],"significant":[9],"financial":[10],"markets,":[11],"foreign":[13],"exchange":[14],"(Forex)":[15],"market":[16],"has":[17,29,70,102],"attracted":[18],"a":[19,31,45,50,87,89,114,174,182,197,357],"large":[20],"number":[21],"investors.":[23],"Accurately":[24],"anticipating":[25],"forex":[27],"trend":[28,194,230],"remained":[30],"popular":[32],"but":[33],"difficult":[34],"issue":[35],"to":[36,75,79,132,145,172,191,206,224,264],"aid":[37],"Forex":[38,51,84,148,229],"traders'":[39],"trading":[40,149],"decisions.":[41],"It":[42],"always":[44],"question":[46],"how":[48,266],"precise":[49],"prediction":[52,231],"can":[53,160],"be":[54,76,161],"because":[55],"market's":[58],"tremendous":[59],"complexity.":[60],"The":[61,105,166,200,273,339],"fast":[62],"advancement":[63],"machine":[65],"learning":[66,135],"in":[67,359],"recent":[68],"decades":[69],"allowed":[71],"artificial":[72,118],"neural":[73,110,119,139,189],"networks":[74],"effectively":[77],"adapted":[78],"several":[80],"areas,":[81],"including":[82],"market.":[85],"As":[86],"result,":[88],"slew":[90],"research":[92],"articles":[93],"aimed":[94],"at":[95],"improving":[96],"accuracy":[98,227],"currency":[100],"forecasting":[101],"been":[103],"released.":[104],"Long":[106,185,279,296],"Short-Term":[107,186,280,297],"Memory":[108,187],"(LSTM)":[109],"network,":[111],"which":[112,354],"special":[115],"kind":[116],"network":[120,140,190],"developed":[121],"exclusively":[122],"for":[123],"time":[124],"series":[125],"data":[126,320],"analysis,":[127],"frequently":[129],"used.":[130],"Due":[131],"its":[133],"high":[134],"capacity,":[136],"LSTM":[138],"increasingly":[142],"being":[143],"utilized":[144],"predict":[146],"advanced":[147],"based":[150,323],"on":[151,156,300,324,369],"previous":[152],"data.":[153],"This":[154],"model,":[155],"other":[158],"hand,":[159],"improved":[162],"by":[163],"stacking":[164],"it.":[165],"goal":[167],"this":[169],"study":[170,242],"choose":[173],"dataset":[175],"using":[176],"Hurst":[178,201,326],"exponent,":[179],"then":[180],"use":[181],"two-layer":[183],"stacked":[184],"(TLS-LSTM)":[188],"forecast":[192],"and":[195,214,235,250,255,259,271,285,312,337,346,352,364],"conduct":[196],"correlation":[198,241,340,374],"analysis.":[199],"exponent":[202,327],"(h)":[203],"was":[204,243,275],"used":[205],"determine":[207],"predictability":[209],"Australian":[212,233,252,257],"Dollar":[213,217,234,238,253,258],"United":[215,236],"States":[216,237],"(AUD/USD)":[218],"dataset.":[219],"TLS-LSTM":[220],"algorithm":[221],"presented":[223],"improve":[225],"(AUD/USD).":[239],"A":[240],"performed":[244],"between":[245,349],"AUD/USD,":[247,350],"Euro":[249],"(EUR/AUD),":[254],"Japanese":[261],"Yen":[262],"(AUD/JPY)":[263],"see":[265],"AUD/USD":[267,360],"movement":[268],"affects":[269],"EUR/AUD":[270,363],"AUD/JPY.":[272],"model":[274],"compared":[276],"with":[277,291],"Single-Layer":[278],"(SL-LSTM),":[281],"Multilayer":[282],"Perceptron":[283],"(MLP),":[284],"Complete":[286],"Ensemble":[287],"Empirical":[288],"Mode":[289],"Decomposition":[290],"Adaptive":[292],"Noise\u2013Improved":[293],"Firefly":[294],"Algorithm":[295],"Memory.":[298],"Based":[299],"evaluation":[302],"metrics":[303],"Mean":[304,309,313],"Square":[305,310],"Error":[306],"(MSE),":[307],"Root":[308],"Error,":[311,315],"Absolute":[314],"suggested":[317],"TLS-LSTM,":[318],"whose":[319],"selection":[321],"(":[328],"h":[329],")":[330],"value":[331],"0.6026,":[333],"outperforms":[334],"SL-LSTM,":[335],"MLP,":[336],"CEEMDAN-IFALSTM.":[338],"analysis":[341],"conducted":[342],"shows":[343],"both":[344],"positive":[345],"negative":[347],"relations":[348],"EUR/AUD,":[351],"AUD/JPY":[353,365],"means":[355],"that":[356],"change":[358],"will":[361],"affect":[362],"as":[366],"recorded":[367],"depending":[368],"magnitude":[371],"coefficient":[375],"(r).":[376]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2022-07-02T00:00:00"}