{"id":"https://openalex.org/W7131063828","doi":"https://doi.org/10.48550/arxiv.2602.17865","title":"Financial time series augmentation using transformer based GAN architecture","display_name":"Financial time series augmentation using transformer based GAN architecture","publication_year":2026,"publication_date":"2026-02-19","ids":{"openalex":"https://openalex.org/W7131063828","doi":"https://doi.org/10.48550/arxiv.2602.17865"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.17865","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126592379","display_name":"Andrzej Podobi\u0144ski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Podobi\u0144ski, Andrzej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5008057050","display_name":"Jaros\u0142aw A. Chudziak","orcid":"https://orcid.org/0000-0003-4534-8652"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chudziak, Jaros\u0142aw A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.7944999933242798,"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":0.7944999933242798,"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":0.05689999833703041,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.051899999380111694,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.6090999841690063},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5640000104904175},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5311999917030334},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.508899986743927},{"id":"https://openalex.org/keywords/scarcity","display_name":"Scarcity","score":0.4580000042915344},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3871999979019165},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.3483000099658966},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3440999984741211},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.34360000491142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6751999855041504},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.6090999841690063},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5640000104904175},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5311999917030334},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.508899986743927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5008000135421753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.4580000042915344},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3750999867916107},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.34360000491142273},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3343000113964081},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C139043278","wikidata":"https://www.wikidata.org/wiki/Q837171","display_name":"Financial services","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C110593043","wikidata":"https://www.wikidata.org/wiki/Q7300787","display_name":"Real-time data","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C23925645","wikidata":"https://www.wikidata.org/wiki/Q5449731","display_name":"Financial modeling","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C97713585","wikidata":"https://www.wikidata.org/wiki/Q6770534","display_name":"Mark to model","level":5,"score":0.2563000023365021},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.25429999828338623}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.17865","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.17865","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.17865","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.17865","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.5225203633308411}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"forecasting":[1,80,120,138,162],"is":[2,31,85],"a":[3,86,98,115,123,131,167,181],"critical":[4],"task":[5],"across":[6,150],"many":[7],"domains,":[8],"from":[9],"engineering":[10],"to":[11,34,64,72,102,141,188],"economics,":[12],"where":[13],"accurate":[14],"predictions":[15],"drive":[16],"strategic":[17],"decisions.":[18],"However,":[19],"applying":[20],"advanced":[21],"deep":[22,78],"learning":[23,79],"models":[24],"in":[25,50,61,106,214],"challenging,":[26],"volatile":[27],"domains":[28],"like":[29],"finance":[30],"difficult":[32],"due":[33],"the":[35,74,107,137,191,196,199,208],"inherent":[36],"limitation":[37],"and":[38,54,156,160,180,194],"dynamic":[39],"nature":[40],"of":[41,77,88,198,210],"financial":[42,68,108,152,216],"time":[43,69,153,169],"series":[44,70,154,170],"data.":[45,201],"This":[46],"scarcity":[47,105],"often":[48],"results":[49,149],"sub-optimal":[51],"model":[52,121],"training":[53,114,192],"poor":[55],"generalization.":[56],"The":[57],"fundamental":[58],"challenge":[59],"lies":[60],"determining":[62],"how":[63,89],"reliably":[65,189],"augment":[66],"scarce":[67],"data":[71,99,104,128,144,212],"enhance":[73],"predictive":[75,217],"accuracy":[76,139],"models.":[81],"Our":[82],"main":[83],"contribution":[84],"demonstration":[87],"Generative":[90],"Adversarial":[91],"Networks":[92],"(GANs)":[93],"can":[94],"effectively":[95],"serve":[96],"as":[97],"augmentation":[100,213],"tool":[101],"overcome":[103],"domain.":[109],"Specifically,":[110],"we":[111,165],"show":[112],"that":[113,174],"Long":[116],"Short-Term":[117],"Memory":[118],"(LSTM)":[119],"on":[122],"dataset":[124],"augmented":[125],"with":[126],"synthetic":[127],"generated":[129,200],"by":[130],"transformer-based":[132],"GAN":[133],"(TTS-GAN)":[134],"significantly":[135],"improves":[136],"compared":[140],"using":[142],"real":[143],"alone.":[145],"We":[146],"confirm":[147],"these":[148],"different":[151],"(Bitcoin":[155],"S\\&P500":[157],"price":[158],"data)":[159],"various":[161],"horizons.":[163],"Furthermore,":[164],"propose":[166],"novel,":[168],"specific":[171],"quality":[172,197],"metric":[173],"combines":[175],"Dynamic":[176],"Time":[177],"Warping":[178],"(DTW)":[179],"modified":[182],"Deep":[183],"Dataset":[184],"Dissimilarity":[185],"Measure":[186],"(DeD-iMs)":[187],"monitor":[190],"progress":[193],"evaluate":[195],"These":[202],"findings":[203],"provide":[204],"compelling":[205],"evidence":[206],"for":[207],"benefits":[209],"GAN-based":[211],"enhancing":[215],"capabilities.":[218]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-24T00:00:00"}