{"id":"https://openalex.org/W4391305528","doi":"https://doi.org/10.1109/access.2024.3359413","title":"Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series","display_name":"Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391305528","doi":"https://doi.org/10.1109/access.2024.3359413"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3359413","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3359413","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10415421.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10415421.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077653649","display_name":"Daniel Gonz\u00e1lez Cort\u00e9s","orcid":"https://orcid.org/0000-0002-5170-9883"},"institutions":[{"id":"https://openalex.org/I1289910757","display_name":"NEOMA Business School","ror":"https://ror.org/01g1pe685","country_code":"FR","type":"education","lineage":["https://openalex.org/I1289910757"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Daniel Gonz\u00e1lez Cort\u00e9s","raw_affiliation_strings":["NEOMA Business School, Mont-Saint-Aignan, France"],"raw_orcid":"https://orcid.org/0000-0002-5170-9883","affiliations":[{"raw_affiliation_string":"NEOMA Business School, Mont-Saint-Aignan, France","institution_ids":["https://openalex.org/I1289910757"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039534258","display_name":"Enrique Onieva","orcid":"https://orcid.org/0000-0001-9581-1823"},"institutions":[{"id":"https://openalex.org/I136040515","display_name":"Universidad de Deusto","ror":"https://ror.org/00ne6sr39","country_code":"ES","type":"education","lineage":["https://openalex.org/I136040515"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Enrique Onieva","raw_affiliation_strings":["Faculty of Engineering, University of Deusto, Bilbao, Spain"],"raw_orcid":"https://orcid.org/0000-0001-9581-1823","affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Deusto, Bilbao, Spain","institution_ids":["https://openalex.org/I136040515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008372294","display_name":"Iker Pastor-L\u00f3pez","orcid":"https://orcid.org/0000-0002-3068-6248"},"institutions":[{"id":"https://openalex.org/I136040515","display_name":"Universidad de Deusto","ror":"https://ror.org/00ne6sr39","country_code":"ES","type":"education","lineage":["https://openalex.org/I136040515"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Iker Pastor L\u00f3pez","raw_affiliation_strings":["Faculty of Engineering, University of Deusto, Bilbao, Spain"],"raw_orcid":"https://orcid.org/0000-0002-3068-6248","affiliations":[{"raw_affiliation_string":"Faculty of Engineering, University of Deusto, Bilbao, Spain","institution_ids":["https://openalex.org/I136040515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074071709","display_name":"Laura Trinchera","orcid":"https://orcid.org/0000-0001-9679-0956"},"institutions":[{"id":"https://openalex.org/I1289910757","display_name":"NEOMA Business School","ror":"https://ror.org/01g1pe685","country_code":"FR","type":"education","lineage":["https://openalex.org/I1289910757"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Laura Trinchera","raw_affiliation_strings":["NEOMA Business School, Mont-Saint-Aignan, France"],"raw_orcid":"https://orcid.org/0000-0001-9679-0956","affiliations":[{"raw_affiliation_string":"NEOMA Business School, Mont-Saint-Aignan, France","institution_ids":["https://openalex.org/I1289910757"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101734435","display_name":"Jian Wu","orcid":"https://orcid.org/0000-0002-0855-1881"},"institutions":[{"id":"https://openalex.org/I1289910757","display_name":"NEOMA Business School","ror":"https://ror.org/01g1pe685","country_code":"FR","type":"education","lineage":["https://openalex.org/I1289910757"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jian Wu","raw_affiliation_strings":["NEOMA Business School, Mont-Saint-Aignan, France"],"raw_orcid":"https://orcid.org/0000-0002-0855-1881","affiliations":[{"raw_affiliation_string":"NEOMA Business School, Mont-Saint-Aignan, France","institution_ids":["https://openalex.org/I1289910757"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":4.1838,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.94640114,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"16999","last_page":"17009"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9975000023841858,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7770451903343201},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7230110168457031},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6767150163650513},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5954337120056152},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5682265162467957},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5655834674835205},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5431451201438904},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.46190696954727173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3586539626121521},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32028937339782715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21597325801849365},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16283050179481506},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15422877669334412},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.11001434922218323}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7770451903343201},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7230110168457031},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6767150163650513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5954337120056152},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5682265162467957},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5655834674835205},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5431451201438904},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.46190696954727173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3586539626121521},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32028937339782715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21597325801849365},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16283050179481506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15422877669334412},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.11001434922218323},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3359413","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3359413","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10415421.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:04c239e64151499aac3ed9a8a80092f9","is_oa":true,"landing_page_url":"https://doaj.org/article/04c239e64151499aac3ed9a8a80092f9","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,"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":"IEEE Access, Vol 12, Pp 16999-17009 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3359413","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3359413","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10415421.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391305528.pdf","grobid_xml":"https://content.openalex.org/works/W4391305528.grobid-xml"},"referenced_works_count":76,"referenced_works":["https://openalex.org/W1738124305","https://openalex.org/W1894414046","https://openalex.org/W1980831577","https://openalex.org/W1991892016","https://openalex.org/W2020814153","https://openalex.org/W2035838643","https://openalex.org/W2064630666","https://openalex.org/W2064675550","https://openalex.org/W2100495367","https://openalex.org/W2101234009","https://openalex.org/W2130942839","https://openalex.org/W2136655611","https://openalex.org/W2146577751","https://openalex.org/W2470039937","https://openalex.org/W2490662969","https://openalex.org/W2523498403","https://openalex.org/W2526420136","https://openalex.org/W2559479590","https://openalex.org/W2737277608","https://openalex.org/W2763583057","https://openalex.org/W2766636840","https://openalex.org/W2772155884","https://openalex.org/W2775015656","https://openalex.org/W2775489129","https://openalex.org/W2784815318","https://openalex.org/W2790117078","https://openalex.org/W2790269004","https://openalex.org/W2795302640","https://openalex.org/W2804498673","https://openalex.org/W2886965520","https://openalex.org/W2888477829","https://openalex.org/W2890474333","https://openalex.org/W2892035503","https://openalex.org/W2893196123","https://openalex.org/W2938507687","https://openalex.org/W2944615123","https://openalex.org/W2951381561","https://openalex.org/W2963166639","https://openalex.org/W2977664641","https://openalex.org/W2999896861","https://openalex.org/W3002694930","https://openalex.org/W3004999940","https://openalex.org/W3006558068","https://openalex.org/W3007784962","https://openalex.org/W3012499112","https://openalex.org/W3039352388","https://openalex.org/W3048718703","https://openalex.org/W3049085523","https://openalex.org/W3089471837","https://openalex.org/W3099347911","https://openalex.org/W3107587362","https://openalex.org/W3110121680","https://openalex.org/W3111435301","https://openalex.org/W3118577826","https://openalex.org/W3122785968","https://openalex.org/W3151864675","https://openalex.org/W3157818903","https://openalex.org/W3159528373","https://openalex.org/W3165577760","https://openalex.org/W3189794255","https://openalex.org/W3202265119","https://openalex.org/W3202302059","https://openalex.org/W3209582350","https://openalex.org/W4200335629","https://openalex.org/W4200448243","https://openalex.org/W4205575767","https://openalex.org/W4206121598","https://openalex.org/W4210764266","https://openalex.org/W4317233797","https://openalex.org/W4387448553","https://openalex.org/W6675354045","https://openalex.org/W6679436768","https://openalex.org/W6685777803","https://openalex.org/W6727682512","https://openalex.org/W6779988842","https://openalex.org/W6801839972"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W4310873165","https://openalex.org/W2355395139","https://openalex.org/W4285596704","https://openalex.org/W3095373396"],"abstract_inverted_index":{"While":[0],"Machine":[1],"Learning":[2],"significantly":[3,108],"boosts":[4],"the":[5,30,44,110,130,139],"performance":[6],"of":[7,25,70,114,120],"predictive":[8,145],"models,":[9,146],"its":[10],"efficacy":[11],"varies":[12],"across":[13],"different":[14],"data":[15,24,38],"dimensions.":[16],"It":[17],"is":[18],"essential":[19],"to":[20,43,88],"cluster":[21],"time":[22,36,72,122],"series":[23,37],"similar":[26],"characteristics,":[27],"particularly":[28],"in":[29],"financial":[31,35,71,90,121,131,144],"sector.":[32],"However,":[33],"clustering":[34,59,84,135],"poses":[39],"considerable":[40],"challenges":[41],"due":[42],"market\u2019s":[45],"inherent":[46],"complexity":[47],"and":[48,83,98,112,154],"multidimensionality.":[49],"To":[50],"address":[51],"these":[52],"issues,":[53],"our":[54,77],"study":[55],"introduces":[56],"a":[57,65],"novel":[58],"framework":[60],"that":[61,105],"leverages":[62],"autoencoders":[63,107],"for":[64,129,141,150],"compressed":[66],"yet":[67],"informative":[68],"representation":[69],"series.":[73,123],"We":[74],"rigorously":[75],"evaluate":[76],"approach":[78],"through":[79],"multiple":[80],"dimensionality":[81],"reduction":[82],"algorithms,":[85],"applying":[86],"it":[87],"key":[89],"indices,":[91],"including":[92],"IBEX-35,":[93],"CAC-40,":[94],"DAX-30,":[95],"S&P":[96],"500,":[97],"FTSE":[99],"100.":[100],"Our":[101,124],"findings":[102,125],"consistently":[103],"demonstrate":[104],"incorporating":[106],"enhances":[109],"granularity":[111],"quality":[113],"clustering,":[115],"effectively":[116],"isolating":[117],"distinct":[118],"categories":[119],"carry":[126],"significant":[127],"ramifications":[128],"industry.":[132],"By":[133],"refining":[134],"methodologies,":[136],"we":[137],"set":[138],"stage":[140],"increasingly":[142],"accurate":[143],"offering":[147],"valuable":[148],"insights":[149],"optimizing":[151],"investment":[152],"strategies":[153],"enhancing":[155],"risk":[156],"management.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-29T08:53:18.405633","created_date":"2025-10-10T00:00:00"}
