{"id":"https://openalex.org/W4392902931","doi":"https://doi.org/10.1109/icassp48485.2024.10448031","title":"Multivariate Time Series Forecasting with Causal-Temporal Attention Network","display_name":"Multivariate Time Series Forecasting with Causal-Temporal Attention Network","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392902931","doi":"https://doi.org/10.1109/icassp48485.2024.10448031"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10448031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448031","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5100330686","display_name":"Wenbo Liu","orcid":"https://orcid.org/0000-0003-2560-0777"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenbo Liu","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China,200438"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China,200438","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119246894","display_name":"Yifan He","orcid":"https://orcid.org/0000-0002-6000-3946"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan He","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China,200438"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China,200438","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086316879","display_name":"Jihong Guan","orcid":"https://orcid.org/0000-0003-2313-7635"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jihong Guan","raw_affiliation_strings":["Tongji University,Department of Computer Science and Technology,Shanghai,China,201804"],"affiliations":[{"raw_affiliation_string":"Tongji University,Department of Computer Science and Technology,Shanghai,China,201804","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017862559","display_name":"Shuigeng Zhou","orcid":"https://orcid.org/0000-0002-1949-2768"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuigeng Zhou","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China,200438"],"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China,200438","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100330686"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":2.0636,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.86693869,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5735","last_page":"5739"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994999766349792,"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":0.9994999766349792,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.8113448619842529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7090942859649658},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6427465677261353},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.570968747138977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5546980500221252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5474331378936768},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5473792552947998},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5307198762893677},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.519263505935669},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5127933621406555},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48384276032447815},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.48104098439216614},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.428377240896225},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1816384494304657}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8113448619842529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7090942859649658},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6427465677261353},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.570968747138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5546980500221252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5474331378936768},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5473792552947998},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5307198762893677},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.519263505935669},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5127933621406555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48384276032447815},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.48104098439216614},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.428377240896225},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1816384494304657},{"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},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10448031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448031","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.46000000834465027,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2293634267","https://openalex.org/W2604847698","https://openalex.org/W2756203131","https://openalex.org/W2808914202","https://openalex.org/W2954731415","https://openalex.org/W2963358464","https://openalex.org/W3000499162","https://openalex.org/W3080253043","https://openalex.org/W3103720336","https://openalex.org/W3177318507","https://openalex.org/W3190469032","https://openalex.org/W3212890323","https://openalex.org/W4210922522","https://openalex.org/W4223926961","https://openalex.org/W4295838474","https://openalex.org/W4297733535","https://openalex.org/W4382239356","https://openalex.org/W4385245566","https://openalex.org/W6746015598","https://openalex.org/W6764679822","https://openalex.org/W6771626834","https://openalex.org/W6797155008","https://openalex.org/W6810637551","https://openalex.org/W6889955440"],"related_works":["https://openalex.org/W1611624937","https://openalex.org/W2072483141","https://openalex.org/W2950035905","https://openalex.org/W2218619375","https://openalex.org/W3123305972","https://openalex.org/W4249439171","https://openalex.org/W2964187211","https://openalex.org/W3100495793","https://openalex.org/W2753729722","https://openalex.org/W3098638521"],"abstract_inverted_index":{"The":[0],"task":[1],"of":[2,43,57,79,120],"multivariate":[3],"time":[4],"series":[5],"(MTS)":[6],"forecasting":[7,31,41,91],"has":[8],"attracted":[9],"much":[10],"attention":[11],"in":[12,125],"recent":[13],"years.":[14],"However,":[15],"most":[16],"existing":[17],"methods":[18],"overlook":[19],"the":[20,40,54,58,70,74,90,94,108,118,123],"causal":[21,49,55,71],"relationship":[22],"among":[23],"different":[24],"variables,":[25],"which":[26],"may":[27],"lead":[28],"to":[29,52,68,88,116],"inaccurate":[30],"results.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"incorporate":[37],"causality":[38],"into":[39,76],"procedure":[42],"MTS.":[44],"We":[45],"first":[46],"use":[47],"a":[48,63,77,83],"discovery":[50],"algorithm":[51],"obtain":[53],"graph":[56,72],"MTS":[59,75],"and":[60,73,122],"then":[61],"design":[62],"novel":[64],"Causal-Temporal":[65],"Attention":[66],"Mechanism":[67],"encode":[69],"set":[78],"feature":[80,95],"tensors.":[81,96],"Finally,":[82],"linear":[84],"decoder":[85],"is":[86],"adopted":[87],"derive":[89],"results":[92,98],"from":[93],"Experiment":[97],"on":[99],"five":[100],"real-world":[101],"datasets":[102],"indicate":[103],"that":[104],"our":[105,126],"method":[106],"outperforms":[107],"state-of-the-art":[109],"models.":[110],"Moreover,":[111],"extra":[112],"experiments":[113],"are":[114],"conducted":[115],"validate":[117],"effectiveness":[119],"hyperparameters":[121],"modules":[124],"method.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}