{"id":"https://openalex.org/W4390494796","doi":"https://doi.org/10.1109/cisp-bmei60920.2023.10373365","title":"Causalformer: Causal Discovery-based Transformer for Multivariate Time Series Forecasting","display_name":"Causalformer: Causal Discovery-based Transformer for Multivariate Time Series Forecasting","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4390494796","doi":"https://doi.org/10.1109/cisp-bmei60920.2023.10373365"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei60920.2023.10373365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei60920.2023.10373365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5090551673","display_name":"Chonghao Zhang","orcid":"https://orcid.org/0009-0007-6051-1303"},"institutions":[{"id":"https://openalex.org/I1334884943","display_name":"Yanbian University of Science and Technology","ror":"https://ror.org/032se3k78","country_code":"CN","type":"education","lineage":["https://openalex.org/I1334884943"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chonghao Zhang","raw_affiliation_strings":["Yanbian University,Department of Computer Science and Technology,Yanji,China","Department of Computer Science and Technology, Yanbian University, Yanji, China"],"affiliations":[{"raw_affiliation_string":"Yanbian University,Department of Computer Science and Technology,Yanji,China","institution_ids":["https://openalex.org/I1334884943"]},{"raw_affiliation_string":"Department of Computer Science and Technology, Yanbian University, Yanji, China","institution_ids":["https://openalex.org/I1334884943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101875722","display_name":"Linlin Zhao","orcid":"https://orcid.org/0009-0003-9628-6997"},"institutions":[{"id":"https://openalex.org/I1334884943","display_name":"Yanbian University of Science and Technology","ror":"https://ror.org/032se3k78","country_code":"CN","type":"education","lineage":["https://openalex.org/I1334884943"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlin Zhao","raw_affiliation_strings":["Yanbian University,Department of Computer Science and Technology,Yanji,China","Department of Computer Science and Technology, Yanbian University, Yanji, China"],"affiliations":[{"raw_affiliation_string":"Yanbian University,Department of Computer Science and Technology,Yanji,China","institution_ids":["https://openalex.org/I1334884943"]},{"raw_affiliation_string":"Department of Computer Science and Technology, Yanbian University, Yanji, China","institution_ids":["https://openalex.org/I1334884943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108245937","display_name":"Zhefeng Yin","orcid":"https://orcid.org/0009-0006-0546-595X"},"institutions":[{"id":"https://openalex.org/I1334884943","display_name":"Yanbian University of Science and Technology","ror":"https://ror.org/032se3k78","country_code":"CN","type":"education","lineage":["https://openalex.org/I1334884943"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhefeng Yin","raw_affiliation_strings":["Yanbian University,Department of Computer Science and Technology,Yanji,China","Department of Computer Science and Technology, Yanbian University, Yanji, China"],"affiliations":[{"raw_affiliation_string":"Yanbian University,Department of Computer Science and Technology,Yanji,China","institution_ids":["https://openalex.org/I1334884943"]},{"raw_affiliation_string":"Department of Computer Science and Technology, Yanbian University, Yanji, China","institution_ids":["https://openalex.org/I1334884943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102876132","display_name":"Zhenguo Zhang","orcid":"https://orcid.org/0009-0007-7108-7878"},"institutions":[{"id":"https://openalex.org/I1334884943","display_name":"Yanbian University of Science and Technology","ror":"https://ror.org/032se3k78","country_code":"CN","type":"education","lineage":["https://openalex.org/I1334884943"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenguo Zhang","raw_affiliation_strings":["Yanbian University,Department of Computer Science and Technology,Yanji,China","Department of Computer Science and Technology, Yanbian University, Yanji, China"],"affiliations":[{"raw_affiliation_string":"Yanbian University,Department of Computer Science and Technology,Yanji,China","institution_ids":["https://openalex.org/I1334884943"]},{"raw_affiliation_string":"Department of Computer Science and Technology, Yanbian University, Yanji, China","institution_ids":["https://openalex.org/I1334884943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5090551673"],"corresponding_institution_ids":["https://openalex.org/I1334884943"],"apc_list":null,"apc_paid":null,"fwci":1.0166,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77492646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9987999796867371,"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":0.9987999796867371,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.992900013923645,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9904999732971191,"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/computer-science","display_name":"Computer science","score":0.6671165227890015},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6631948947906494},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.587929904460907},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.49941396713256836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43200185894966125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4140702784061432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3888559937477112}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6671165227890015},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6631948947906494},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.587929904460907},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.49941396713256836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43200185894966125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4140702784061432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3888559937477112},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei60920.2023.10373365","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei60920.2023.10373365","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8799999952316284,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2020126697","https://openalex.org/W2096023955","https://openalex.org/W2127888717","https://openalex.org/W2138905229","https://openalex.org/W2157331557","https://openalex.org/W2160973997","https://openalex.org/W2604847698","https://openalex.org/W2792764867","https://openalex.org/W2896457183","https://openalex.org/W2908623803","https://openalex.org/W2954731415","https://openalex.org/W2963460797","https://openalex.org/W2963532813","https://openalex.org/W2964099165","https://openalex.org/W3036859210","https://openalex.org/W3094502228","https://openalex.org/W3133777397","https://openalex.org/W3171665133","https://openalex.org/W3177318507","https://openalex.org/W3212890323","https://openalex.org/W3214860080","https://openalex.org/W4214698730","https://openalex.org/W4245651501","https://openalex.org/W4361020001","https://openalex.org/W4382237561","https://openalex.org/W4385245566","https://openalex.org/W4385763767","https://openalex.org/W6683237687","https://openalex.org/W6739901393","https://openalex.org/W6749825310","https://openalex.org/W6755207826","https://openalex.org/W6763045174","https://openalex.org/W6764679822","https://openalex.org/W6784333009","https://openalex.org/W6797155008","https://openalex.org/W6797297377","https://openalex.org/W6810637551","https://openalex.org/W6889955440"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W1919101720","https://openalex.org/W1570805059","https://openalex.org/W2118640767","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"The":[0],"forecasting":[1],"of":[2,46,60,126,215,224],"time":[3,35,100,180],"series":[4,181],"is":[5,157],"finding":[6],"increasingly":[7],"widespread":[8],"applications":[9],"in":[10,103,183],"real-world":[11],"scenarios,":[12],"such":[13],"as":[14,144,172],"medical":[15],"data":[16],"and":[17,26,56,77,212],"electricity":[18],"consumption.":[19],"Recent":[20],"work":[21],"primarily":[22],"employs":[23],"the":[24,44,51,58,64,82,110,124,141,149,163,170,203,213],"Transformer":[25,69],"its":[27],"variant":[28,70],"to":[29,75,118,122,147,176],"capture":[30],"broad":[31],"temporal":[32,47,89],"dependencies":[33],"from":[34,98,222],"series.":[36],"However,":[37],"most":[38,201],"approaches":[39],"often":[40],"overly":[41],"focus":[42],"on":[43,72,197],"impact":[45],"relation,":[48],"while":[49],"neglecting":[50],"interactions":[52],"among":[53,128],"multivariate":[54,99,129],"elements,":[55,130],"lacking":[57],"application":[59],"causal":[61,73,79,111,120,150,216],"relationships.":[62],"Int":[63],"paper,":[65],"we":[66,139,167],"introduce":[67],"a":[68,94,104,184],"based":[71],"discovery":[74,217],"discover":[76],"utilize":[78,148],"relationships,":[80],"termed":[81],"Causalformer.":[83],"Our":[84],"Causalformer":[85],"not":[86,219],"only":[87],"captures":[88],"features":[90],"but":[91],"also":[92,168],"learns":[93],"Granger":[95],"causality":[96],"graph":[97],"series,":[101],"resulting":[102],"sparse":[105],"adjacency":[106],"matrix":[107,151],"that":[108,223],"represents":[109],"relationships":[112,121],"between":[113],"variables.":[114],"This":[115],"allows":[116],"us":[117],"leverage":[119],"estimate":[123],"degree":[125],"influence":[127],"thereby":[131],"further":[132],"enhancing":[133],"forecast":[134,204],"accuracy.":[135],"Building":[136],"upon":[137],"this,":[138],"configure":[140],"decoder":[142,171],"part":[143],"multi-headed":[145],"output":[146,177],"for":[152],"understanding":[153],"how":[154],"each":[155],"variable":[156],"influenced":[158],"by":[159],"others.":[160],"To":[161],"accelerate":[162],"model\u2019s":[164],"learning":[165],"speed,":[166],"design":[169],"generative,":[173],"allowing":[174],"it":[175],"an":[178],"extended":[179],"sequence":[182],"single":[185],"shot":[186],"rather":[187],"than":[188,209],"step-by-step,":[189],"significantly":[190,207,220],"improving":[191],"inference":[192],"efficiency.":[193],"We":[194],"conduct":[195],"experiments":[196],"three":[198],"datasets.":[199],"In":[200],"cases,":[202],"results":[205],"were":[206],"better":[208],"other":[210],"baselines,":[211],"accuracy":[214],"was":[218],"different":[221],"SOTA.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}