{"id":"https://openalex.org/W4416232998","doi":"https://doi.org/10.1145/3768292.3770402","title":"BForTFin: A Financial Domain-Aware Multiscale Evaluation Method for Time-Series Foundation Models","display_name":"BForTFin: A Financial Domain-Aware Multiscale Evaluation Method for Time-Series Foundation Models","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W4416232998","doi":"https://doi.org/10.1145/3768292.3770402"},"language":null,"primary_location":{"id":"doi:10.1145/3768292.3770402","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3768292.3770402","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3768292.3770402","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120498637","display_name":"Nigel Cheong","orcid":"https://orcid.org/0009-0004-8073-7877"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nigel Cheong","raw_affiliation_strings":["NTU, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"NTU, Singapore, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120548893","display_name":"Ling Wei Hsuen","orcid":"https://orcid.org/0009-0003-3668-191X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ling Wei Hsuen","raw_affiliation_strings":["NTU, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"NTU, Singapore, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054901394","display_name":"Ranjan Satapathy","orcid":"https://orcid.org/0000-0002-0733-7381"},"institutions":[{"id":"https://openalex.org/I4210161496","display_name":"A*STAR Graduate Academy","ror":"https://ror.org/059yjzn93","country_code":"SG","type":"education","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210161496"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Satapathy Ranjan","raw_affiliation_strings":["A*STAR-IHPC, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"A*STAR-IHPC, Singapore, Singapore","institution_ids":["https://openalex.org/I4210161496"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100752356","display_name":"Erik Cambria","orcid":"https://orcid.org/0000-0002-3030-1280"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erik Cambria","raw_affiliation_strings":["NTU, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"NTU, Singapore, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001599295","display_name":"Rick Siow Mong Goh","orcid":"https://orcid.org/0000-0001-9116-1595"},"institutions":[{"id":"https://openalex.org/I4210161496","display_name":"A*STAR Graduate Academy","ror":"https://ror.org/059yjzn93","country_code":"SG","type":"education","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210161496"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Rick Siow Mong Goh","raw_affiliation_strings":["A*STAR-IHPC, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"A*STAR-IHPC, Singapore, Singapore","institution_ids":["https://openalex.org/I4210161496"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069083106","display_name":"Joyjit Chattoraj","orcid":"https://orcid.org/0000-0003-1910-8954"},"institutions":[{"id":"https://openalex.org/I4210161496","display_name":"A*STAR Graduate Academy","ror":"https://ror.org/059yjzn93","country_code":"SG","type":"education","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210161496"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Joyjit Chattoraj","raw_affiliation_strings":["A*STAR-IHPC, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"A*STAR-IHPC, Singapore, Singapore","institution_ids":["https://openalex.org/I4210161496"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5120498637"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1106,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90909859,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"274","last_page":"282"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.5019000172615051,"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":0.5019000172615051,"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":0.1445000022649765,"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":0.12489999830722809,"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/benchmarking","display_name":"Benchmarking","score":0.7283999919891357},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5389000177383423},{"id":"https://openalex.org/keywords/interdependence","display_name":"Interdependence","score":0.5336999893188477},{"id":"https://openalex.org/keywords/asset","display_name":"Asset (computer security)","score":0.5044000148773193},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.45239999890327454},{"id":"https://openalex.org/keywords/financial-modeling","display_name":"Financial modeling","score":0.4381999969482422},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4277999997138977},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.35440000891685486}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7283999919891357},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.597000002861023},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5389000177383423},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.5336999893188477},{"id":"https://openalex.org/C76178495","wikidata":"https://www.wikidata.org/wiki/Q4808784","display_name":"Asset (computer security)","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.5005999803543091},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.45239999890327454},{"id":"https://openalex.org/C23925645","wikidata":"https://www.wikidata.org/wiki/Q5449731","display_name":"Financial modeling","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3946000039577484},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.35440000891685486},{"id":"https://openalex.org/C139043278","wikidata":"https://www.wikidata.org/wiki/Q837171","display_name":"Financial services","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3456999957561493},{"id":"https://openalex.org/C84749600","wikidata":"https://www.wikidata.org/wiki/Q5449714","display_name":"Financial engineering","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29330000281333923},{"id":"https://openalex.org/C139819358","wikidata":"https://www.wikidata.org/wiki/Q462748","display_name":"Asset allocation","level":3,"score":0.28940001130104065},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C70179600","wikidata":"https://www.wikidata.org/wiki/Q1416547","display_name":"Financial plan","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2581999897956848},{"id":"https://openalex.org/C2778705799","wikidata":"https://www.wikidata.org/wiki/Q2823610","display_name":"Financial asset","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C130731218","wikidata":"https://www.wikidata.org/wiki/Q1363554","display_name":"Financial analysis","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3768292.3770402","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3768292.3770402","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3768292.3770402","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3768292.3770402","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2121224351","https://openalex.org/W2768149277","https://openalex.org/W2980994438","https://openalex.org/W4281857486","https://openalex.org/W4310527582","https://openalex.org/W4377230452","https://openalex.org/W4378674930","https://openalex.org/W4379115199","https://openalex.org/W4392917200","https://openalex.org/W4393177791","https://openalex.org/W4402670306","https://openalex.org/W4404781017","https://openalex.org/W4409441551","https://openalex.org/W4410356856","https://openalex.org/W4414715578","https://openalex.org/W7103748692"],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"foundation":[1,214],"models":[2,29,195],"(TSFMs)":[3],"have":[4],"emerged":[5],"as":[6],"powerful":[7],"tools":[8],"for":[9,166,215,222],"forecasting,":[10],"offering":[11,211],"the":[12,46,53,69,91,164,175,191,202,216],"advantage":[13],"of":[14,56,93,144,204,218],"learning":[15],"universal":[16],"temporal":[17],"representations":[18],"that":[19,30,153,173,193],"generalize":[20],"across":[21,109,158],"diverse":[22],"tasks":[23],"and":[24,52,71,128,134,161],"domains,":[25],"unlike":[26],"traditional":[27],"AI":[28],"require":[31],"task-specific":[32],"training.":[33],"However,":[34],"applying":[35],"TSFMs":[36,94,145,219],"to":[37,45,64,89],"financial":[38,50,66,96,223],"time":[39],"series":[40],"forecasting":[41],"remains":[42],"challenging":[43,190],"due":[44],"complex":[47],"interdependencies":[48],"among":[49],"instruments":[51,67],"limited":[54],"diversity":[55],"existing":[57],"pretraining":[58],"datasets,":[59],"which":[60,207],"are":[61],"often":[62],"restricted":[63],"major":[65],"from":[68],"U.S.":[70],"a":[72,84,101,121,141,212],"few":[73],"other":[74],"developed":[75],"markets.":[76],"To":[77],"address":[78],"these":[79,147],"gaps,":[80],"this":[81],"study":[82,151],"presents":[83],"comprehensive":[85],"benchmarking":[86],"method,":[87],"BForTFin,":[88],"assess":[90],"effectiveness":[92],"in":[95,183],"forecasting:":[97],"First,":[98],"it":[99,119,138],"constructs":[100],"finance":[102],"domain-aware":[103],"dataset":[104],"comprising":[105],"seven":[106],"asset":[107,159],"classes":[108,160],"over":[110],"50":[111],"countries":[112],"spanning":[113],"nine":[114],"global":[115],"economic":[116],"regions.":[117],"Second,":[118],"introduces":[120],"multiscale":[122],"evaluation":[123],"protocol":[124],"covering":[125],"daily,":[126],"weekly,":[127],"monthly":[129],"frequencies,":[130,189],"incorporating":[131],"both":[132],"statistical":[133],"structural":[135],"metrics.":[136],"Third,":[137],"systematically":[139],"evaluates":[140],"representative":[142],"set":[143],"on":[146],"curated":[148],"datasets.":[149],"The":[150],"reveals":[152],"model":[154,177],"performance":[155],"varies":[156],"significantly":[157],"timescales,":[162],"underscoring":[163],"need":[165],"domain-specific":[167],"evaluation.":[168],"A":[169],"surprising":[170],"finding":[171],"is":[172],"Lag-Llama,":[174],"smallest":[176],"evaluated,":[178],"consistently":[179],"delivers":[180],"superior":[181],"accuracy":[182],"several":[184],"scenarios,":[185],"particularly":[186],"at":[187],"higher":[188],"assumption":[192],"larger":[194],"inherently":[196],"perform":[197],"better.":[198],"These":[199],"findings":[200],"highlight":[201],"importance":[203],"rigorous":[205],"benchmarking,":[206],"BForTFin":[208],"provides":[209],"by":[210],"reproducible":[213],"comparison":[217],"specifically":[220],"designed":[221],"forecasting.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-11-14T00:00:00"}