{"id":"https://openalex.org/W4404411451","doi":"https://doi.org/10.1109/tbdata.2024.3499312","title":"Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series","display_name":"Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series","publication_year":2024,"publication_date":"2024-11-15","ids":{"openalex":"https://openalex.org/W4404411451","doi":"https://doi.org/10.1109/tbdata.2024.3499312"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2024.3499312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2024.3499312","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017388383","display_name":"Xin Bi","orcid":"https://orcid.org/0000-0002-2645-1112"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]},{"id":"https://openalex.org/I1327237609","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934","country_code":"CN","type":"funder","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xin Bi","raw_affiliation_strings":["Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China","Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]},{"raw_affiliation_string":"Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, China","institution_ids":["https://openalex.org/I1327237609"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009402084","display_name":"Quan Jin","orcid":"https://orcid.org/0000-0002-3020-5504"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghan Jin","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101566003","display_name":"Mark A. J. Song","orcid":"https://orcid.org/0000-0001-5053-5490"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiling Song","raw_affiliation_strings":["College of Software, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Software, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701308","display_name":"Xin Yao","orcid":"https://orcid.org/0000-0002-7292-1410"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yao","raw_affiliation_strings":["College of Software, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Software, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011679038","display_name":"Xiangguo Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangguo Zhao","raw_affiliation_strings":["College of Software, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Software, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014346487","display_name":"Ye Yuan","orcid":"https://orcid.org/0000-0002-0247-9866"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054991337","display_name":"Guoren Wang","orcid":"https://orcid.org/0000-0002-0181-8379"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoren Wang","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5017388383"],"corresponding_institution_ids":["https://openalex.org/I1327237609","https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23310847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"4","first_page":"1589","last_page":"1599"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9664999842643738,"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":0.9664999842643738,"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/computer-science","display_name":"Computer science","score":0.7477519512176514},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.72743159532547},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6288021802902222},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5477213263511658},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4757799208164215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36330336332321167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7477519512176514},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.72743159532547},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6288021802902222},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5477213263511658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4757799208164215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36330336332321167},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2024.3499312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2024.3499312","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2808664059","https://openalex.org/W2996954980","https://openalex.org/W3026442639","https://openalex.org/W3036859210","https://openalex.org/W3177318507","https://openalex.org/W3198281649","https://openalex.org/W4205170483","https://openalex.org/W4225150471","https://openalex.org/W4226429038","https://openalex.org/W4289711940","https://openalex.org/W4293146286","https://openalex.org/W4310214223","https://openalex.org/W4310987264","https://openalex.org/W4312105465","https://openalex.org/W4312222186","https://openalex.org/W4312734973","https://openalex.org/W4313505511","https://openalex.org/W4317666316","https://openalex.org/W4319987556","https://openalex.org/W4322101461","https://openalex.org/W4361011496","https://openalex.org/W4377824108","https://openalex.org/W4378347262","https://openalex.org/W4381620679","https://openalex.org/W4381827695","https://openalex.org/W4382240245","https://openalex.org/W4385285268","https://openalex.org/W4387662440","https://openalex.org/W4387818788","https://openalex.org/W4388069241","https://openalex.org/W4388486421","https://openalex.org/W4391250751","https://openalex.org/W4391486486","https://openalex.org/W4392309297","https://openalex.org/W4392767931","https://openalex.org/W4392942193","https://openalex.org/W4393147209","https://openalex.org/W4393147362","https://openalex.org/W4393159722","https://openalex.org/W6774218346","https://openalex.org/W6774554947","https://openalex.org/W6789171369","https://openalex.org/W6794504039","https://openalex.org/W6797155008","https://openalex.org/W6810637551"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W2118640767","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"In":[0,17],"multivariate":[1,24,82,91,181],"time":[2,25,83,92,182],"series":[3,26,84,93,183],"prediction":[4,15,67,85,147,184],"tasks,":[5],"the":[6,23,47,58,101,108,112,116,131,146,150],"inter-":[7],"and":[8,20,39,155,185],"intra-variable":[9],"relations":[10,104],"have":[11],"significant":[12],"influence":[13],"on":[14,100],"outcomes.":[16],"many":[18],"engineering":[19],"industrial":[21],"scenarios,":[22],"also":[27],"contain":[28],"a":[29,75,87,136],"large":[30],"number":[31],"of":[32,41,49,60,153],"subjective":[33,51,109,132,137,143,154],"influencing":[34],"factors,":[35],"such":[36],"as":[37],"settings":[38],"behaviors":[40],"users.":[42],"Existing":[43],"learning":[44,59,89,118,133],"methods":[45],"neglect":[46],"interactions":[48],"these":[50],"factors":[52,110,157],"among":[53,105],"variables.":[54],"This":[55],"leads":[56],"to":[57,125,141,158,173],"incorrect":[61],"inter-variable":[62],"influences,":[63],"consequently":[64],"yielding":[65],"inaccurate":[66],"results.":[68],"To":[69],"address":[70],"this":[71],"challenge,":[72],"we":[73],"propose":[74],"Decoupled":[76],"Casal":[77],"Attention":[78],"Network":[79],"(DECA)":[80],"for":[81,190],"from":[86,111],"spatiotemporal":[88],"perspective.":[90],"prediction.":[94],"The":[95,161],"causality":[96],"decoupling":[97],"module,":[98],"based":[99],"captured":[102],"causal":[103,123,138],"variables,":[106],"disentangles":[107],"objective":[113,117,122,127,156],"factors.":[114],"Then":[115],"module":[119,134,148],"utilizes":[120,135],"an":[121],"attention":[124,140],"capture":[126,142],"cross-variable":[128],"dependencies;":[129],"while":[130],"graph":[139],"influences.":[144],"Finally,":[145],"fuses":[149],"multi-scale":[151],"features":[152],"produce":[159],"predictions.":[160],"performance":[162],"is":[163],"evaluated":[164],"using":[165],"three":[166],"benchmark":[167],"datasets.":[168],"Results":[169],"indicate":[170],"that,":[171],"compared":[172],"state-of-the-art":[174],"methods,":[175],"DECA":[176],"exhibits":[177],"superior":[178],"accuracy":[179],"in":[180],"can":[186],"be":[187],"effectively":[188],"used":[189],"recommendations.":[191]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}