{"id":"https://openalex.org/W4416018141","doi":"https://doi.org/10.1145/3746252.3760962","title":"Channel-Independent Refiner for Multivariate Time Series Forecasting","display_name":"Channel-Independent Refiner for Multivariate Time Series Forecasting","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416018141","doi":"https://doi.org/10.1145/3746252.3760962"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3760962","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3760962","pdf_url":null,"source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746252.3760962","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100440059","display_name":"Jie Wang","orcid":"https://orcid.org/0000-0002-2567-8761"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Wang","raw_affiliation_strings":["FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2567-8761","affiliations":[{"raw_affiliation_string":"FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112138154","display_name":"Zhongguang Zheng","orcid":"https://orcid.org/0009-0003-2091-0933"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongguang Zheng","raw_affiliation_strings":["FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-2091-0933","affiliations":[{"raw_affiliation_string":"FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065997113","display_name":"Chaoliang Zhong","orcid":"https://orcid.org/0000-0003-1697-6932"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoliang Zhong","raw_affiliation_strings":["FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1697-6932","affiliations":[{"raw_affiliation_string":"FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100327195","display_name":"Jun Sun","orcid":"https://orcid.org/0000-0002-0967-4859"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Sun","raw_affiliation_strings":["FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0967-4859","affiliations":[{"raw_affiliation_string":"FUJITSU RESEARCH & DEVELOPMENT CENTER CO.,LTD., Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100440059"],"corresponding_institution_ids":["https://openalex.org/I4210159607"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39297599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5304","last_page":"5308"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.5544999837875366,"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":0.5544999837875366,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.15379999577999115,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.08020000159740448,"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/multivariate-statistics","display_name":"Multivariate statistics","score":0.7759000062942505},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.7468000054359436},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6585999727249146},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6266000270843506},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5544000267982483},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.5277000069618225}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7759000062942505},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.7468000054359436},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6585999727249146},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6266000270843506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6126999855041504},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.5277000069618225},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.499099999666214},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48669999837875366},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29319998621940613},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2687999904155731},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2639000117778778}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3760962","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3760962","pdf_url":null,"source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3746252.3760962","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3760962","pdf_url":null,"source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1983883318","https://openalex.org/W3010192349","https://openalex.org/W3177318507","https://openalex.org/W4312472971","https://openalex.org/W4379382937","https://openalex.org/W4382203079"],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"time":[1,30],"series":[2,31],"data":[3],"are":[4,13,18],"usually":[5],"multivariate":[6,29],"with":[7,19,139],"complex":[8],"channel":[9,37,43],"relations.":[10],"Some":[11],"channels":[12],"highly":[14],"related,":[15],"while":[16],"others":[17],"limited":[20],"correlation.":[21],"Channel":[22],"independence":[23],"has":[24],"shown":[25],"great":[26],"significance":[27],"in":[28,129],"forecasting":[32,121],"to":[33,41,62],"capture":[34],"better":[35,86],"individual":[36],"characteristics.":[38],"In":[39],"order":[40],"learn":[42],"patterns":[44],"better,":[45],"we":[46,56],"propose":[47],"a":[48,52,58,85,97],"channel-independent":[49,94],"Refiner":[50,60,83,89,119,137],"as":[51],"plug-and-play":[53],"module.":[54],"Specifically,":[55],"devise":[57],"channel-wise":[59],"connected":[61],"the":[63,69,75,79,82,93,133,142],"output":[64],"of":[65,71,132],"existing":[66],"methods.":[67],"Leveraging":[68],"concatenation":[70],"original":[72],"input":[73],"and":[74,96,114,126],"coarse":[76],"prediction":[77],"from":[78,92],"base":[80,104],"model,":[81],"produces":[84],"estimation.":[87],"Our":[88,136],"also":[90],"benefits":[91],"design":[95],"post-training":[98],"strategy,":[99],"achieving":[100],"significant":[101],"improvement":[102],"over":[103,130],"models.":[105],"Extensive":[106],"experiments":[107],"on":[108,123],"iTransformer,":[109],"FEDformer,":[110],"Autoformer,":[111],"FreTS,":[112],"DLinear":[113],"TSMixer":[115],"demonstrate":[116],"that":[117],"our":[118],"reduces":[120],"errors":[122],"both":[124],"Transformer-based":[125],"MLP-based":[127],"models":[128],"90%":[131],"experimental":[134],"settings.":[135],"combined":[138],"iTransformer":[140],"establishes":[141],"new":[143],"state-of-the-art.":[144]},"counts_by_year":[],"updated_date":"2025-11-08T23:25:12.792448","created_date":"2025-11-08T00:00:00"}