{"id":"https://openalex.org/W4415428368","doi":"https://doi.org/10.3233/faia251166","title":"Transferability-Driven Variable Recalibration for Multivariate Time Series Domain Adaptation","display_name":"Transferability-Driven Variable Recalibration for Multivariate Time Series Domain Adaptation","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415428368","doi":"https://doi.org/10.3233/faia251166"},"language":null,"primary_location":{"id":"doi:10.3233/faia251166","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251166","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251166","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kexuan Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kexuan Zhou","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080113737","display_name":"Jing Geng","orcid":"https://orcid.org/0000-0003-1775-3490"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajing Geng","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Boliang Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Boliang Hao","raw_affiliation_strings":["School of Computer Science and Technology, Zhejiang Sci-Tech University, HangZhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Zhejiang Sci-Tech University, HangZhou, China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086628680","display_name":"Bailing Zhang","orcid":"https://orcid.org/0000-0002-5472-2100"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]},{"id":"https://openalex.org/I159389169","display_name":"Ningbo University of Technology","ror":"https://ror.org/037dym702","country_code":"CN","type":"education","lineage":["https://openalex.org/I159389169"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bailing Zhang","raw_affiliation_strings":["Department of Computer Science and Data Engineering, NingboTech University, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Data Engineering, NingboTech University, Ningbo, China","institution_ids":["https://openalex.org/I159389169","https://openalex.org/I109935558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63468925,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.8658000230789185,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.8658000230789185,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.761900007724762,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.7134000062942505,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.623199999332428},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5383999943733215},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5339000225067139},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.5170000195503235},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.5105999708175659},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.49390000104904175},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46709999442100525},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.430400013923645},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.42829999327659607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6391000151634216},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.623199999332428},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5383999943733215},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5339000225067139},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.5170000195503235},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.5105999708175659},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.49390000104904175},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4465000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4372999966144562},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.430400013923645},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.42829999327659607},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4214000105857849},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3450999855995178},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2540000081062317},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251166","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251166","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251166","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251166","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unsupervised":[0],"Domain":[1],"Adaptation":[2],"(UDA)":[3],"seeks":[4],"to":[5,13,21,48,155,171],"transfer":[6],"knowledge":[7],"from":[8,32,202],"a":[9,105,123,148],"labeled":[10],"source":[11,204],"domain":[12,111,192,205],"an":[14],"unlabeled":[15],"target":[16,191],"domain.":[17],"However,":[18],"when":[19],"applied":[20],"multivariate":[22,82],"time":[23,83,214],"series":[24,84,215],"(MTS)":[25,85],"data,":[26],"UDA":[27,42],"faces":[28],"unique":[29],"challenges":[30],"arising":[31],"the":[33,55,190,203,207],"inherent":[34],"complexity":[35],"of":[36],"sensor-generated":[37],"signals.":[38],"While":[39],"most":[40],"existing":[41,222],"approaches":[43],"rely":[44],"on":[45,212],"feature":[46],"extractors":[47],"capture":[49],"global":[50],"representations,":[51],"they":[52],"often":[53],"overlook":[54],"heterogeneous":[56],"distribution":[57],"shifts":[58],"across":[59,96],"individual":[60],"variables\u2014an":[61],"issue":[62],"primarily":[63],"caused":[64],"by":[65,118,195],"variations":[66],"in":[67,81,109,189,206],"sensor":[68],"properties":[69],"and":[70,87,99,175],"recording":[71],"conditions.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76,121],"systematically":[77],"investigate":[78],"variable-level":[79],"transferability":[80,174],"data":[86],"report":[88],"two":[89],"key":[90],"findings:":[91],"(1)":[92,136],"Transferability":[93,162],"varies":[94],"significantly":[95],"different":[97],"variables,":[98],"(2)":[100,160],"aligning":[101,196],"marginal":[102,158],"distributions":[103,179],"plays":[104],"more":[106],"crucial":[107],"role":[108],"reducing":[110],"discrepancy":[112],"than":[113],"adapting":[114],"conditional":[115],"distributions.":[116],"Motivated":[117],"these":[119],"insights,":[120],"propose":[122],"novel":[124],"method":[125],"called":[126],"Transferability-Driven":[127],"Variable":[128],"Recalibration":[129],"(TDVR),":[130],"which":[131],"comprises":[132],"three":[133],"core":[134],"components:":[135],"Variable-Specific":[137],"Marginal":[138],"Distribution":[139],"Modeling":[140],"(VSMDM):":[141],"Each":[142],"variable":[143],"is":[144],"individually":[145],"processed":[146],"using":[147],"dedicated":[149],"1D":[150],"convolutional":[151],"neural":[152],"network":[153],"(1D-CNN)":[154],"extract":[156],"domain-invariant":[157],"features;":[159],"Quantitative":[161],"Alignment":[163],"(QTA):":[164],"We":[165],"leverage":[166],"Maximum":[167],"Mean":[168],"Discrepancy":[169],"(MMD)":[170],"measure":[172],"variable-wise":[173],"dynamically":[176],"recalibrate":[177],"their":[178],"accordingly;":[180],"(3)":[181],"Prototype-Guided":[182],"Adaptive":[183],"Fusion":[184],"(PGAF):":[185],"During":[186],"inference,":[187],"predictions":[188],"are":[193],"refined":[194],"them":[197],"with":[198],"class-specific":[199],"prototypes":[200],"derived":[201],"latent":[208],"space.":[209],"Extensive":[210],"experiments":[211],"diverse":[213],"benchmarks":[216],"demonstrate":[217],"that":[218],"TDVR":[219],"consistently":[220],"outperforms":[221],"methods,":[223],"achieving":[224],"new":[225],"state-of-the-art":[226],"performance.":[227]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-24T00:00:00"}
