{"id":"https://openalex.org/W4392200358","doi":"https://doi.org/10.18280/isi.290114","title":"Long-Term Forecasting of Euro-Dollar Exchange Rates Using the ARIMA Model and Multilayer Perceptron","display_name":"Long-Term Forecasting of Euro-Dollar Exchange Rates Using the ARIMA Model and Multilayer Perceptron","publication_year":2024,"publication_date":"2024-02-27","ids":{"openalex":"https://openalex.org/W4392200358","doi":"https://doi.org/10.18280/isi.290114"},"language":"fr","primary_location":{"id":"doi:10.18280/isi.290114","doi":"https://doi.org/10.18280/isi.290114","is_oa":true,"landing_page_url":"https://doi.org/10.18280/isi.290114","pdf_url":"https://iieta.org/download/file/fid/120302","source":{"id":"https://openalex.org/S1757003","display_name":"Ing\u00e9nierie des syst\u00e8mes d information","issn_l":"1633-1311","issn":["1633-1311","2116-7125"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310311056","host_organization_name":"Lavoisier publishing","host_organization_lineage":["https://openalex.org/P4310311056"],"host_organization_lineage_names":["Lavoisier publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ing\u00e9nierie des syst\u00e8mes d information","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://iieta.org/download/file/fid/120302","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094013402","display_name":"Gladys R. Velasco Chasipanta","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210145045","display_name":"Universidad Polit\u00e9cnica Estatal del Carchi","ror":"https://ror.org/04msd0t92","country_code":"EC","type":"education","lineage":["https://openalex.org/I4210145045"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Gladys R. Velasco Chasipanta","raw_affiliation_strings":["Postgraduate Center, Carchi State Polytechnic University, Tulc\u00e1 n 040101, Ecuador"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Postgraduate Center, Carchi State Polytechnic University, Tulc\u00e1 n 040101, Ecuador","institution_ids":["https://openalex.org/I4210145045"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017650015","display_name":"Nadia N. S\u00e1nchez-Pozo","orcid":"https://orcid.org/0000-0003-0509-2082","observed_orcids":["https://orcid.org/0000-0003-0509-2082"]},"institutions":[{"id":"https://openalex.org/I4210145045","display_name":"Universidad Polit\u00e9cnica Estatal del Carchi","ror":"https://ror.org/04msd0t92","country_code":"EC","type":"education","lineage":["https://openalex.org/I4210145045"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Nadia N. S\u00e1nchez-Pozo","raw_affiliation_strings":["Postgraduate Center, Carchi State Polytechnic University, Tulc\u00e1 n 040101, Ecuador"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Postgraduate Center, Carchi State Polytechnic University, Tulc\u00e1 n 040101, Ecuador","institution_ids":["https://openalex.org/I4210145045"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210145045"],"apc_list":null,"apc_paid":null,"fwci":0.6656,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67527419,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":"29","issue":"1","first_page":"125","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.5839999914169312,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.5839999914169312,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.41499999165534973,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/euro-dollar-exchange-rate","display_name":"euro-dollar exchange rate","score":0.7620000243186951},{"id":"https://openalex.org/keywords/arima","display_name":"ARIMA","score":0.7940000295639038},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":1.0},{"id":"https://openalex.org/keywords/neural-network-forecasting","display_name":"neural network forecasting","score":0.6349999904632568},{"id":"https://openalex.org/keywords/nonlinear-time-series","display_name":"nonlinear time series","score":0.5709999799728394},{"id":"https://openalex.org/keywords/lag-length-selection","display_name":"lag selection","score":0.640999972820282},{"id":"https://openalex.org/keywords/forecasting-model-comparison","display_name":"forecasting model comparison","score":0.4399999976158142}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8590180277824402},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7161300182342529},{"id":"https://openalex.org/C3019931467","wikidata":"https://www.wikidata.org/wiki/Q4917","display_name":"Us dollar","level":3,"score":0.625983476638794},{"id":"https://openalex.org/C109168655","wikidata":"https://www.wikidata.org/wiki/Q242988","display_name":"Liberian dollar","level":2,"score":0.5536959171295166},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.539395272731781},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.46682092547416687},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.44150015711784363},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.3982633650302887},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3503299951553345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3358769416809082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2535959482192993},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.21988174319267273},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20360156893730164},{"id":"https://openalex.org/C556758197","wikidata":"https://www.wikidata.org/wiki/Q580018","display_name":"Monetary economics","level":1,"score":0.20205360651016235},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.07210013270378113},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06715849041938782},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18280/isi.290114","doi":"https://doi.org/10.18280/isi.290114","is_oa":true,"landing_page_url":"https://doi.org/10.18280/isi.290114","pdf_url":"https://iieta.org/download/file/fid/120302","source":{"id":"https://openalex.org/S1757003","display_name":"Ing\u00e9nierie des syst\u00e8mes d information","issn_l":"1633-1311","issn":["1633-1311","2116-7125"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310311056","host_organization_name":"Lavoisier publishing","host_organization_lineage":["https://openalex.org/P4310311056"],"host_organization_lineage_names":["Lavoisier publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ing\u00e9nierie des syst\u00e8mes d information","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.18280/isi.290114","doi":"https://doi.org/10.18280/isi.290114","is_oa":true,"landing_page_url":"https://doi.org/10.18280/isi.290114","pdf_url":"https://iieta.org/download/file/fid/120302","source":{"id":"https://openalex.org/S1757003","display_name":"Ing\u00e9nierie des syst\u00e8mes d information","issn_l":"1633-1311","issn":["1633-1311","2116-7125"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310311056","host_organization_name":"Lavoisier publishing","host_organization_lineage":["https://openalex.org/P4310311056"],"host_organization_lineage_names":["Lavoisier publishing"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ing\u00e9nierie des syst\u00e8mes d information","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/W4392200358.pdf","grobid_xml":"https://content.openalex.org/works/W4392200358.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1551187515","https://openalex.org/W2131534673","https://openalex.org/W2149905014","https://openalex.org/W2150000631","https://openalex.org/W2159651898","https://openalex.org/W2624385633","https://openalex.org/W2791884409","https://openalex.org/W2794624256","https://openalex.org/W2794778778","https://openalex.org/W2807149171","https://openalex.org/W2901769706","https://openalex.org/W2911494597","https://openalex.org/W2913836720","https://openalex.org/W3007066689","https://openalex.org/W3008376342","https://openalex.org/W3016536355","https://openalex.org/W3039200459","https://openalex.org/W3107775714","https://openalex.org/W3180293405","https://openalex.org/W4205861619","https://openalex.org/W4206471037","https://openalex.org/W4213206733","https://openalex.org/W4220945596","https://openalex.org/W4312895731","https://openalex.org/W4379984205","https://openalex.org/W4386759999","https://openalex.org/W4391108721","https://openalex.org/W7043043864"],"related_works":["https://openalex.org/W4402617373","https://openalex.org/W4401997298","https://openalex.org/W6998777704","https://openalex.org/W3174944207","https://openalex.org/W7163164312","https://openalex.org/W2283684474","https://openalex.org/W4407666414","https://openalex.org/W2913626262","https://openalex.org/W4386687306","https://openalex.org/W7147631242"],"abstract_inverted_index":{"Forecasting":[0],"exchange":[1,16,45,51,119,132,150],"rates":[2,52],"is":[3,36],"a":[4,87,127,143],"complex":[5,13,155],"problem":[6],"due":[7],"to":[8,37,55,111,153,160],"the":[9,60,73,96,100,105,118],"inherent":[10],"volatility":[11],"and":[12,63,68,115,158],"dynamics":[14],"of":[15,33,49,93,135,149],"rates.Traditional":[17],"forecasting":[18,41,148],"models":[19,65,141],"such":[20],"as":[21],"ARIMA":[22,106],"often":[23],"cannot":[24],"capture":[25,112,154],"these":[26],"complexities":[27],"especially":[28],"for":[29,43,59,130,146,168],"long-term":[30,44,147],"forecasts.The":[31],"objective":[32],"this":[34,136,169],"study":[35,137],"develop":[38],"an":[39],"accurate":[40],"model":[42,76,102,124],"rates.A":[46],"data":[47,98],"set":[48],"eurodollar":[50],"from":[53,83],"2017":[54],"2022":[56],"was":[57],"used":[58],"present":[61],"analysis.ARIMA":[62],"MLP":[64,75,101,123,140],"were":[66,71],"developed":[67],"their":[69],"performances":[70],"compared;":[72],"optimized":[74,122],"equipped":[77],"with":[78],"11":[79],"input":[80],"neurons":[81],"derived":[82],"significant":[84],"lags":[85],"achieved":[86],"scaled":[88],"mean":[89],"absolute":[90],"error":[91],"(MASE)":[92],"0.75":[94],"on":[95],"test":[97],"while":[99],"significantly":[103],"outperformed":[104],"model,":[107],"demonstrating":[108],"its":[109],"ability":[110,152],"underlying":[113],"patterns":[114],"trends":[116],"in":[117],"rate":[120],"data.The":[121],"also":[125],"provided":[126],"365-day":[128],"forecast":[129],"2023":[131],"rates.The":[133],"results":[134],"suggest":[138],"that":[139],"are":[142],"promising":[144],"tool":[145],"rates.Their":[151],"nonlinear":[156],"relationships":[157],"adapt":[159],"changing":[161],"market":[162],"conditions":[163],"makes":[164],"them":[165],"well":[166],"suited":[167],"challenging":[170],"task.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2024-02-28T00:00:00"}