{"id":"https://openalex.org/W7171535524","doi":"https://doi.org/10.48550/arxiv.2607.23503","title":"Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments","display_name":"Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments","publication_year":2026,"publication_date":"2026-07-26","ids":{"openalex":"https://openalex.org/W7171535524","doi":"https://doi.org/10.48550/arxiv.2607.23503"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.23503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23503","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.23503","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143771814","display_name":"Zhichen Lai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lai, Zhichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143836332","display_name":"Huan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Huan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143866422","display_name":"Dalin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Dalin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143790472","display_name":"Dong Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Dong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143806383","display_name":"Lina Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Lina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143829150","display_name":"Christian S. Jensen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jensen, Christian S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.492000013589859,"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":0.492000013589859,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.18119999766349792,"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"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.07609999924898148,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6301000118255615},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.6151000261306763},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.589900016784668},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5613999962806702},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.5547000169754028},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4733999967575073},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4528999924659729},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.43650001287460327},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.3774000108242035}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7470999956130981},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6301000118255615},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6151000261306763},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.589900016784668},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5802000164985657},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5613999962806702},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37560001015663147},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35749998688697815},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3361999988555908},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.23503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23503","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.23503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23503","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5020993947982788,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Internet":[0],"of":[1,8,147],"Things":[2],"(IoT)":[3],"applications":[4],"generate":[5],"vast":[6],"amounts":[7],"Correlated":[9],"Time":[10],"Series":[11],"(CTS)":[12],"data":[13,138],"that":[14,50,140],"often":[15,27],"contain":[16],"missing":[17],"values":[18],"and":[19,46,86,128,136,163,166],"require":[20],"imputation.":[21],"Existing":[22],"methods":[23],"emphasize":[24],"accuracy":[25],"but":[26],"lack":[28],"adaptability":[29,126],"to":[30,37,54,84,96,115,150],"changing":[31,69],"IoT":[32],"environments:":[33],"they":[34],"are":[35],"vulnerable":[36],"sensor":[38,98],"failures,":[39],"cannot":[40],"selectively":[41],"impute":[42],"only":[43],"incomplete":[44],"sensors,":[45],"use":[47],"static":[48],"architectures":[49],"do":[51],"not":[52],"adapt":[53],"resource":[55],"availability.":[56],"To":[57],"address":[58],"these":[59],"limitations,":[60],"we":[61],"propose":[62],"AdaCTSi,":[63],"an":[64,145],"adaptive":[65],"CTS":[66],"imputer":[67],"for":[68],"environments.":[70],"AdaCTSi":[71,141],"combines":[72],"a":[73,79],"One-shot":[74],"Temporal":[75],"Convolutional":[76],"Network":[77],"with":[78,121],"Learned":[80],"Time-Sensor":[81],"Index":[82],"Table":[83],"extract":[85],"decouple":[87],"complex":[88],"spatio-temporal":[89],"features":[90],"into":[91],"sensor-wise":[92],"embeddings,":[93],"enabling":[94],"adaptation":[95],"varying":[97],"subsets.":[99],"Sparse":[100],"Spatial":[101],"Attention":[102],"efficiently":[103],"extracts":[104],"dynamic":[105],"spatial":[106,118],"correlations,":[107],"while":[108],"Correlation-Weighted":[109],"Sensor":[110],"Selection":[111],"selects":[112],"informative":[113],"sensors":[114],"provide":[116],"sufficient":[117],"context.":[119],"Experiments":[120],"twelve":[122],"baseline":[123,153],"methods,":[124],"three":[125],"scenarios,":[127],"five":[129],"benchmark":[130],"datasets":[131],"covering":[132],"traffic,":[133],"air":[134],"quality,":[135],"trajectory":[137],"show":[139],"reduces":[142],"MAE":[143],"by":[144],"average":[146],"33.1%":[148],"relative":[149],"the":[151],"strongest":[152],"on":[154,173],"each":[155],"dataset.":[156],"A":[157],"single":[158],"trained":[159],"model":[160],"supports":[161],"sensor-subset":[162],"resource-adaptive":[164],"inference,":[165],"its":[167],"modest":[168],"memory":[169],"footprint":[170],"enables":[171],"deployment":[172],"commodity":[174],"computing":[175],"devices,":[176],"including":[177],"MCUs.":[178]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}
