{"id":"https://openalex.org/W4416017013","doi":"https://doi.org/10.1145/3746252.3761273","title":"Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting","display_name":"Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416017013","doi":"https://doi.org/10.1145/3746252.3761273"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761273","pdf_url":null,"source":null,"license":null,"license_id":null,"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":"conference-paper","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":null,"display_name":"Yifan Gao","orcid":"https://orcid.org/0009-0001-9252-9598"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Gao","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-9252-9598","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103281510","display_name":"Boming Zhao","orcid":"https://orcid.org/0000-0003-3267-6040"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Boming Zhao","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3267-6040","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027575021","display_name":"Haocheng Peng","orcid":"https://orcid.org/0009-0009-7158-1002"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haocheng Peng","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0009-7158-1002","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059021869","display_name":"Hujun Bao","orcid":"https://orcid.org/0000-0002-2662-0334"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hujun Bao","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2662-0334","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011324370","display_name":"Jiashu Zhao","orcid":"https://orcid.org/0000-0001-9770-7616"},"institutions":[{"id":"https://openalex.org/I75381157","display_name":"Wilfrid Laurier University","ror":"https://ror.org/00fn7gb05","country_code":"CA","type":"education","lineage":["https://openalex.org/I75381157"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jiashu Zhao","raw_affiliation_strings":["Wilfrid Laurier University, Waterloo, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9770-7616","affiliations":[{"raw_affiliation_string":"Wilfrid Laurier University, Waterloo, Canada","institution_ids":["https://openalex.org/I75381157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075311772","display_name":"Zhaopeng Cui","orcid":"https://orcid.org/0000-0002-7130-439X"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaopeng Cui","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7130-439X","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"696","last_page":"706"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.4011000096797943,"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":0.4011000096797943,"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":0.13979999721050262,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.1316000074148178,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.8927000164985657},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6583999991416931},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5993000268936157},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5837000012397766},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5519000291824341},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.513700008392334},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.5044999718666077}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.8927000164985657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7843000292778015},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6583999991416931},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5993000268936157},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5837000012397766},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5521000027656555},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.513700008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5084999799728394},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4722000062465668},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2662999927997589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761273","pdf_url":null,"source":null,"license":null,"license_id":null,"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":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G957166398","display_name":null,"funder_award_id":"62441222","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2028045978","https://openalex.org/W2036785686","https://openalex.org/W2990955039","https://openalex.org/W3007066689","https://openalex.org/W3080253043","https://openalex.org/W3084879683","https://openalex.org/W3207999419","https://openalex.org/W4283577641","https://openalex.org/W4306317058","https://openalex.org/W4382239131","https://openalex.org/W4387092352","https://openalex.org/W4401353384","https://openalex.org/W4401567681","https://openalex.org/W4409194799"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,9,107,168],"deep":[3],"learning":[4],"have":[5],"significantly":[6],"boosted":[7],"performance":[8],"multivariate":[10,108],"time":[11,109,142],"series":[12,143],"forecasting":[13,116],"(MTSF).":[14],"While":[15],"many":[16],"existing":[17,63],"approaches":[18],"focus":[19],"on":[20,148,154],"capturing":[21,169],"inter-variable":[22,174],"(a.k.a.":[23],"channel-wise)":[24],"correlations":[25],"to":[26,87,97,126,144],"improve":[27],"prediction":[28],"accuracy,":[29],"the":[30,99],"temporal":[31,70,91,103,150,171],"dimension,":[32],"particularly":[33],"its":[34,166],"rich":[35],"structural":[36],"and":[37,68,101,124,173],"contextual":[38],"information,":[39],"remains":[40],"underexplored.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45,73,111,131],"propose":[46],"BIM3,":[47],"a":[48,75,83,133],"novel":[49,76],"framework":[50],"that":[51,65,118,139,159],"integrates":[52],"BIdirectional":[53],"temporal-aware":[54],"modeling":[55],"with":[56],"Multi-Scale":[57,134],"Mixture-of-Experts":[58,137],"for":[59],"MTSF.":[60],"First,":[61],"unlike":[62],"methods":[64],"treat":[66],"historical":[67],"future":[69],"information":[71],"independently,":[72],"introduce":[74],"Timestamp":[77],"Dual":[78],"Cross-Attention":[79],"Module,":[80],"which":[81],"employs":[82],"symmetric":[84],"cross-attention":[85],"mechanism":[86],"explicitly":[88],"capture":[89,127],"bidirectional":[90],"dependencies":[92],"through":[93],"timestamp":[94],"interactions.":[95],"Second,":[96],"address":[98],"complex":[100],"scale-varying":[102],"patterns":[104],"commonly":[105],"found":[106],"series,":[110],"move":[112],"beyond":[113],"recent":[114],"multi-scale":[115],"models":[117],"share":[119],"parameters":[120],"across":[121],"all":[122],"channels":[123],"fail":[125],"channel-specific":[128],"dynamics.":[129],"Instead,":[130],"design":[132],"Feature":[135],"Extract":[136],"module":[138],"adaptively":[140],"routes":[141],"specialized":[145],"experts":[146],"based":[147],"their":[149],"characteristics.":[151],"Extensive":[152],"experiments":[153],"multiple":[155],"real-world":[156],"datasets":[157],"show":[158],"BIM3":[160],"consistently":[161],"outperforms":[162],"state-of-the-art":[163],"methods,":[164],"highlighting":[165],"effectiveness":[167],"both":[170],"structure":[172],"diversity.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}