{"id":"https://openalex.org/W4416251554","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229402","title":"MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing","display_name":"MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251554","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229402"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100773644","display_name":"Zhe Li","orcid":"https://orcid.org/0009-0003-1018-8190"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhe Li","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087523144","display_name":"Xuanxuan Li","orcid":"https://orcid.org/0000-0002-2171-2969"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuanxuan Li","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012248304","display_name":"Zhongwen Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongwen Rao","raw_affiliation_strings":["Huawei Technologies Ltd,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Huawei Technologies Ltd,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009221204","display_name":"Lujia Pan","orcid":"https://orcid.org/0000-0002-8988-4740"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lujia Pan","raw_affiliation_strings":["Huawei Technologies Ltd,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Huawei Technologies Ltd,Shenzhen,China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051227924","display_name":"Zenglin Xu","orcid":"https://orcid.org/0000-0001-5550-6461"},"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":"Zenglin Xu","raw_affiliation_strings":["Fudan University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100773644"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":4.9832,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95638482,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.7682999968528748,"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":0.7682999968528748,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.06960000097751617,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.05079999938607216,"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/time-series","display_name":"Time series","score":0.6240000128746033},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5914000272750854},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5909000039100647},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5539000034332275},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5514000058174133},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.41920000314712524}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6432999968528748},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6240000128746033},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5909000039100647},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5539000034332275},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5514000058174133},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4336000084877014},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.41920000314712524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40230000019073486},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3264000117778778},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28610000014305115},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1983883318","https://openalex.org/W2117014758","https://openalex.org/W2604847698","https://openalex.org/W2626662725","https://openalex.org/W2963532813","https://openalex.org/W3010192349","https://openalex.org/W3041430681","https://openalex.org/W3162090017","https://openalex.org/W3177318507","https://openalex.org/W4313152909","https://openalex.org/W4382203079","https://openalex.org/W4385767731"],"related_works":[],"abstract_inverted_index":{"Multivariate":[0],"time":[1,71,114],"series":[2,72,115],"forecasting":[3,20,102,120,150],"has":[4],"been":[5],"widely":[6],"used":[7],"in":[8,19,32,92],"various":[9],"practical":[10],"scenarios.":[11],"Recently,":[12],"Transformer-based":[13],"models":[14],"have":[15],"shown":[16],"significant":[17],"potential":[18],"tasks":[21],"due":[22],"to":[23,118,133],"the":[24,33,40,60,68,88,93,101,107,110,119],"capture":[25,94,134],"of":[26,42,64,70,95],"long-range":[27],"dependencies.":[28,138],"However,":[29],"recent":[30],"studies":[31],"vision":[34],"and":[35,62,90,97,104,112,136],"NLP":[36],"fields":[37],"show":[38,145],"that":[39,77,146],"role":[41],"attention":[43,65,79],"modules":[44,132],"is":[45,80,116,156],"not":[46,81],"clear,":[47],"which":[48,128],"can":[49],"be":[50],"replaced":[51],"by":[52],"other":[53],"token":[54],"aggregation":[55],"operations.":[56],"This":[57],"paper":[58],"investigates":[59],"contributions":[61],"deficiencies":[63],"mechanisms":[66],"on":[67,141],"performance":[69],"forecasting.":[73],"Specifically,":[74],"we":[75,125],"find":[76],"(1)":[78],"necessary":[82],"for":[83],"capturing":[84],"temporal":[85,96,135],"dependencies,":[86],"(2)":[87],"entanglement":[89],"redundancy":[91],"channel":[98,137],"interaction":[99],"affect":[100],"performance,":[103],"(3)":[105],"modeling":[106],"mapping":[108],"between":[109],"historical":[111],"predicting":[113],"crucial":[117],"accuracy.":[121],"To":[122],"this":[123],"end,":[124],"propose":[126],"MTS-Mixers,":[127],"use":[129],"two":[130],"factorized":[131],"Experimental":[139],"results":[140,151],"several":[142],"real-world":[143],"datasets":[144],"MTS-Mixers":[147],"performs":[148],"promising":[149],"with":[152],"higher":[153],"efficiency.":[154],"Code":[155],"available":[157],"at":[158],"https://github.com/plumprc/MTS-Mixers.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}