{"id":"https://openalex.org/W4415428319","doi":"https://doi.org/10.3233/faia251117","title":"Dual-Path Adaptive-Correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting","display_name":"Dual-Path Adaptive-Correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415428319","doi":"https://doi.org/10.3233/faia251117"},"language":null,"primary_location":{"id":"doi:10.3233/faia251117","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251117","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251117","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050783869","display_name":"Wenbo Yan","orcid":"https://orcid.org/0000-0002-6977-6283"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Yan","raw_affiliation_strings":["School of Intelligence Science and Technology, Peking University, Beijing 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024152934","display_name":"Shurui Wang","orcid":"https://orcid.org/0009-0005-9887-2281"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shurui Wang","raw_affiliation_strings":["School of Intelligence Science and Technology, Peking University, Beijing 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005546864","display_name":"Ying Tan","orcid":"https://orcid.org/0000-0002-8139-5758"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Tan","raw_affiliation_strings":["Institute for Artificial Intellignce, Peking University, Beijing 100871, China","School of Intelligence Science and Technology, Peking University, Beijing 100871, China","State Key Laboratory of General Artificial Intelligence, Peking University, Beijing 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Artificial Intellignce, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I20231570"]},{"raw_affiliation_string":"School of Intelligence Science and Technology, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"State Key Laboratory of General Artificial Intelligence, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46851927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10320","display_name":"Neural Networks and Applications","score":0.9668999910354614,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9668999910354614,"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"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9266999959945679,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.913100004196167,"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/time-series","display_name":"Time series","score":0.5131999850273132},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.49799999594688416},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.46129998564720154},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.3714999854564667},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.35749998688697815}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5289000272750854},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.49799999594688416},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.46129998564720154},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4043000042438507},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3061999976634979},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2890999913215637},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251117","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251117","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251117","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251117","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Market":[0],"regimes":[1],"are":[2],"a":[3,35,45,79,102],"critical":[4],"factor":[5],"influencing":[6],"stock":[7,118],"price":[8],"fluctuations.":[9],"We":[10],"observe":[11],"that":[12],"regime":[13],"characteristics":[14],"can":[15],"be":[16],"reflected":[17],"in":[18,22,71,136],"the":[19,23,60,82,132],"dynamic":[20,69],"variations":[21,70],"strength":[24],"of":[25,48,62,65,98,128],"multiple":[26,63],"inter-stock":[27],"relationships.":[28],"However,":[29],"existing":[30],"methodologies":[31],"predominantly":[32],"rely":[33],"on":[34,116],"single":[36],"graph":[37],"constructed":[38],"using":[39],"prior":[40],"knowledge":[41],"or":[42],"directly":[43],"infer":[44],"singular":[46],"type":[47],"relationship":[49],"from":[50],"time":[51,91],"series":[52,92],"data.":[53],"These":[54],"approaches":[55],"fail":[56],"to":[57,94,105],"account":[58],"for":[59],"existence":[61],"types":[64,97],"relationships":[66,99],"and":[67,100],"their":[68],"strength.":[72],"To":[73],"address":[74],"this":[75],"limitation,":[76],"we":[77],"propose":[78],"novel":[80],"framework,":[81],"Dual-Path":[83],"Adaptive-Correlation":[84],"Spatial-Temporal":[85],"Inverted":[86],"Transformer":[87],"(DPA-STIFormer),":[88],"which":[89],"decouples":[90],"data":[93],"learn":[95],"diverse":[96],"introduces":[101],"gated":[103],"mechanism":[104],"adaptively":[106],"fuse":[107],"them,":[108],"thereby":[109],"accommodating":[110],"different":[111],"market":[112,119],"regimes.":[113],"Experiments":[114],"conducted":[115],"four":[117],"datasets":[120],"demonstrate":[121],"state-of-the-art":[122],"performance,":[123],"with":[124],"an":[125],"average":[126],"improvement":[127],"over":[129],"5%,":[130],"validating":[131],"model\u2019s":[133],"superior":[134],"capability":[135],"uncovering":[137],"latent":[138],"temporal-correlation":[139],"patterns.":[140]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-24T00:00:00"}