{"id":"https://openalex.org/W4411950073","doi":"https://doi.org/10.1109/iwcmc65282.2025.11059684","title":"An Efficient Timer-Expanded Multi-variate Time Series Forecasting Foundation Model","display_name":"An Efficient Timer-Expanded Multi-variate Time Series Forecasting Foundation Model","publication_year":2025,"publication_date":"2025-05-12","ids":{"openalex":"https://openalex.org/W4411950073","doi":"https://doi.org/10.1109/iwcmc65282.2025.11059684"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc65282.2025.11059684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc65282.2025.11059684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Wireless Communications and Mobile Computing (IWCMC)","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":null,"display_name":"Chang Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chang Lu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101528355","display_name":"Yuan Chang","orcid":"https://orcid.org/0000-0001-7301-2211"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Chang","raw_affiliation_strings":["China Telecom Research Institute,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China Telecom Research Institute,Beijing,China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066104282","display_name":"Aobo Liang","orcid":"https://orcid.org/0009-0003-1607-9067"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aobo Liang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010337399","display_name":"Hong Luo","orcid":"https://orcid.org/0000-0002-0672-3440"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Luo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007091805","display_name":"Yan Sun","orcid":"https://orcid.org/0000-0002-8192-9545"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Sun","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18778515,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"49","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9369000196456909,"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":0.9369000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/timer","display_name":"Timer","score":0.7879002690315247},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6956345438957214},{"id":"https://openalex.org/keywords/random-variate","display_name":"Random variate","score":0.6627732515335083},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6128433346748352},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5609099268913269},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.4245627522468567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4031514525413513},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29461002349853516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1243453323841095},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11981868743896484},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10340410470962524},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.07506632804870605}],"concepts":[{"id":"https://openalex.org/C2776633867","wikidata":"https://www.wikidata.org/wiki/Q186612","display_name":"Timer","level":3,"score":0.7879002690315247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6956345438957214},{"id":"https://openalex.org/C141547133","wikidata":"https://www.wikidata.org/wiki/Q7291996","display_name":"Random variate","level":3,"score":0.6627732515335083},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6128433346748352},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5609099268913269},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.4245627522468567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4031514525413513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29461002349853516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1243453323841095},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11981868743896484},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10340410470962524},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.07506632804870605},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc65282.2025.11059684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc65282.2025.11059684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"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":6,"referenced_works":["https://openalex.org/W1887986397","https://openalex.org/W2268207689","https://openalex.org/W3041430681","https://openalex.org/W3177318507","https://openalex.org/W4391621245","https://openalex.org/W4392106667"],"related_works":["https://openalex.org/W2360439717","https://openalex.org/W2388292776","https://openalex.org/W2765214557","https://openalex.org/W2361181213","https://openalex.org/W1031622928","https://openalex.org/W2350005463","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,30,81],"series":[2,31,82,87],"forecasting":[3],"has":[4,21,72,148],"a":[5,86],"wide":[6],"range":[7],"of":[8,67,88,145],"applications":[9],"in":[10,49,57,76,90,158],"scenarios":[11,50],"such":[12,55,101],"as":[13,56,102],"wireless":[14],"communication":[15],"and":[16,109,129,134],"mobile":[17],"computing.":[18],"Recent":[19],"research":[20],"demonstrated":[22],"that":[23,140],"transformer":[24],"architectures":[25],"are":[26,112],"highly":[27],"effective":[28],"for":[29,80,160],"forecasting,":[32],"with":[33,64,155],"smaller":[34,44],"models":[35,45,79,96],"achieving":[36],"impressive":[37],"results":[38],"across":[39],"multiple":[40,152],"public":[41,153],"datasets.":[42],"However,":[43,93],"tend":[46],"to":[47,85],"underperform":[48],"where":[51],"data":[52],"is":[53],"scarce,":[54],"zero-shot":[58,120,142],"prediction":[59,143],"tasks.":[60],"In":[61,115],"recent":[62],"years,":[63],"the":[65,94,104,124,141],"rise":[66],"large":[68,78],"language":[69],"models,":[70],"there":[71],"been":[73],"growing":[74],"interest":[75],"developing":[77],"prediction,":[83,121],"leading":[84],"advancements":[89],"this":[91,116],"area.":[92],"current":[95],"often":[97],"suffer":[98],"from":[99],"issues":[100],"neglecting":[103],"inter-dependencies":[105],"between":[106],"multi-dimensional":[107],"variables":[108],"their":[110],"operations":[111],"too":[113],"complicated.":[114],"paper,":[117],"focusing":[118],"on":[119,151],"we":[122],"enhance":[123],"Timer":[125],"model":[126,147],"by":[127],"efficiently":[128],"adaptively":[130],"capturing":[131],"multivariate":[132],"dependencies":[133],"frequency":[135],"domain":[136],"dynamics.":[137],"Experiments":[138],"show":[139],"capability":[144],"our":[146],"significantly":[149],"improved":[150],"datasets,":[154],"an":[156],"decrease":[157],"MSE":[159],"over":[161],"10%.":[162]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-10-10T00:00:00"}