{"id":"https://openalex.org/W4404351591","doi":"https://doi.org/10.1145/3677052.3698625","title":"Entity-based Financial Tabular Data Synthesis with Diffusion Models","display_name":"Entity-based Financial Tabular Data Synthesis with Diffusion Models","publication_year":2024,"publication_date":"2024-11-14","ids":{"openalex":"https://openalex.org/W4404351591","doi":"https://doi.org/10.1145/3677052.3698625"},"language":"en","primary_location":{"id":"doi:10.1145/3677052.3698625","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698625","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698625","source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th 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://dl.acm.org/doi/pdf/10.1145/3677052.3698625","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082123119","display_name":"Changshuo Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Changshuo Liu","raw_affiliation_strings":["Strike Technologies LLC, US","Yale University, US"],"raw_orcid":"https://orcid.org/0009-0009-3141-5341","affiliations":[{"raw_affiliation_string":"Strike Technologies LLC, US","institution_ids":[]},{"raw_affiliation_string":"Yale University, US","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068600963","display_name":"Canyao Liu","orcid":"https://orcid.org/0000-0003-3291-247X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Canyao Liu","raw_affiliation_strings":["Strike Technologies LLC, US","Yale University, US"],"raw_orcid":"https://orcid.org/0000-0003-3291-247X","affiliations":[{"raw_affiliation_string":"Strike Technologies LLC, US","institution_ids":[]},{"raw_affiliation_string":"Yale University, US","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082123119"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24395924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"547","last_page":"554"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9958000183105469,"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":0.9958000183105469,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9717000126838684,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.9686999917030334,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/diffusion","display_name":"Diffusion","score":0.6226614713668823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5351349711418152}],"concepts":[{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6226614713668823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5351349711418152},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3677052.3698625","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698625","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698625","source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3677052.3698625","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698625","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698625","source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4404351591.pdf"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W173006792","https://openalex.org/W2970971581","https://openalex.org/W3015694082","https://openalex.org/W3104728961","https://openalex.org/W3111896136","https://openalex.org/W3129829742","https://openalex.org/W3181975995","https://openalex.org/W3202428668","https://openalex.org/W3204818612","https://openalex.org/W3212867051","https://openalex.org/W4220709328","https://openalex.org/W4289520498","https://openalex.org/W4312933868","https://openalex.org/W4320024133","https://openalex.org/W4383823329","https://openalex.org/W4386435775","https://openalex.org/W4388994349","https://openalex.org/W4392903449","https://openalex.org/W4398172486"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"In":[0],"the":[1,6,14,38,64,67,110,130],"rapidly":[2],"evolving":[3],"financial":[4,36,136],"industry,":[5],"adoption":[7],"of":[8,35,40,112,132],"synthetic":[9,25,116],"tabular":[10,26,98,122],"data":[11,19,22,27,80,99,123,144],"is":[12],"on":[13,82,101,125],"rise":[15],"to":[16,66,114],"augment":[17],"scarce":[18],"and":[20,74,118,148],"facilitate":[21],"sharing.":[23],"Existing":[24],"generation":[28],"methods":[29,77],"often":[30],"overlook":[31],"a":[32,41,55,88],"crucial":[33],"aspect":[34],"datasets:":[37],"presence":[39],"special":[42],"column":[43,53],"representing":[44],"entities,":[45,70],"such":[46],"as":[47,54],"companies":[48],"or":[49],"individuals.":[50],"Treating":[51],"this":[52],"mere":[56],"categorical":[57],"feature":[58],"has":[59],"significant":[60],"drawbacks,":[61],"including":[62],"limiting":[63],"model":[65],"original":[68],"dataset\u2019s":[69],"increasing":[71],"privacy":[72],"risks,":[73],"lacking":[75],"efficient":[76],"for":[78],"generating":[79],"conditioned":[81,100],"specific":[83],"entities.":[84,102,127],"We":[85,128],"introduce":[86],"EntTabDiff,":[87],"diffusion":[89],"model-based":[90],"method":[91],"that":[92,108,120,141],"addresses":[93],"these":[94,126],"challenges":[95],"by":[96],"synthesizing":[97],"EntTabDiff":[103,133,142],"comprises":[104],"two":[105],"components:":[106],"one":[107],"learns":[109],"distribution":[111],"entities":[113],"generate":[115],"ones,":[117],"another":[119],"synthesizes":[121,143],"based":[124],"demonstrate":[129],"effectiveness":[131],"using":[134],"three":[135],"datasets.":[137],"Experimental":[138],"results":[139],"show":[140],"with":[145],"high":[146],"utility":[147],"improved":[149],"fidelity.":[150]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}