{"id":"https://openalex.org/W4413074994","doi":"https://doi.org/10.1109/tkde.2025.3589693","title":"iBACon: imBalance-Aware Contrastive Learning for Time Series Forecasting","display_name":"iBACon: imBalance-Aware Contrastive Learning for Time Series Forecasting","publication_year":2025,"publication_date":"2025-07-16","ids":{"openalex":"https://openalex.org/W4413074994","doi":"https://doi.org/10.1109/tkde.2025.3589693"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2025.3589693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3589693","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-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/A5100630818","display_name":"Jing Zhang","orcid":"https://orcid.org/0009-0006-5955-2063"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["College of Information Science and Technology &#x0026; Artificial Intelligence, Nanjing Forestry University, Nanjing, China","College of Information Science and Technology &amp; Artificial Intelligence, Nanjing Forestry University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0006-5955-2063","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology &#x0026; Artificial Intelligence, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]},{"raw_affiliation_string":"College of Information Science and Technology &amp; Artificial Intelligence, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008891189","display_name":"Qun Dai","orcid":"https://orcid.org/0000-0003-4618-7299"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qun Dai","raw_affiliation_strings":["College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-4618-7299","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100728766","display_name":"Rui Ye","orcid":"https://orcid.org/0009-0007-5998-8200"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Ye","raw_affiliation_strings":["College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China","institution_ids":["https://openalex.org/I119454577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8182,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.86176582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"37","issue":"10","first_page":"5967","last_page":"5982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9962000250816345,"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":0.9962000250816345,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9603000283241272,"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/computer-science","display_name":"Computer science","score":0.8133413791656494},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6101629137992859},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6019354462623596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42310112714767456},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39322414994239807}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8133413791656494},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6101629137992859},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6019354462623596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42310112714767456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39322414994239807},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2025.3589693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3589693","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G1899641159","display_name":null,"funder_award_id":"62476126","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":32,"referenced_works":["https://openalex.org/W2163922914","https://openalex.org/W2589114814","https://openalex.org/W2971724044","https://openalex.org/W2990955039","https://openalex.org/W3035725276","https://openalex.org/W3136608531","https://openalex.org/W3154252594","https://openalex.org/W3177318507","https://openalex.org/W3188872815","https://openalex.org/W3190152617","https://openalex.org/W3199148273","https://openalex.org/W4206995085","https://openalex.org/W4225974022","https://openalex.org/W4247367216","https://openalex.org/W4280557312","https://openalex.org/W4288064617","https://openalex.org/W4313887285","https://openalex.org/W4361297541","https://openalex.org/W4366352743","https://openalex.org/W4376286506","https://openalex.org/W4378417908","https://openalex.org/W4382239668","https://openalex.org/W4384157235","https://openalex.org/W4385245566","https://openalex.org/W4385562572","https://openalex.org/W4386473007","https://openalex.org/W4386811916","https://openalex.org/W4388895078","https://openalex.org/W4393153153","https://openalex.org/W4394699135","https://openalex.org/W4401487891","https://openalex.org/W4402056503"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Time":[0],"series":[1],"forecasting":[2],"(TSF)":[3],"has":[4],"gained":[5],"significant":[6],"attention":[7],"as":[8,98],"a":[9,64,99,114,120],"widely":[10],"explored":[11],"research":[12],"area":[13],"in":[14,23,32,39,107,125],"diverse":[15],"applications.":[16],"Existing":[17],"methods,":[18],"which":[19],"focus":[20,28],"on":[21,30,132],"improvements":[22],"the":[24,40,56,93,103,135,181],"most":[25,183],"common":[26],"scenarios,":[27],"little":[29],"performance":[31],"rare":[33,43],"cases.":[34],"Despite":[35],"their":[36],"scarce":[37],"occurrences":[38],"data,":[41],"these":[42],"samples":[44,151],"are":[45],"more":[46],"challenging":[47,150,184],"and":[48,78,101,128,152,178],"easily":[49],"overlooked":[50],"by":[51,73],"models,":[52],"significantly":[53,166],"contributing":[54],"to":[55,84,141,145],"total":[57],"loss.":[58],"In":[59],"this":[60,71,88,133],"paper,":[61],"we":[62,90],"propose":[63],"novel":[65],"approach":[66],"(dubbed":[67],"iBACon)":[68],"that":[69,172],"overcomes":[70],"limitation":[72],"employing":[74],"imbalance-aware":[75,137],"contrastive":[76,138],"learning":[77],"trend-seasonal":[79,162],"decomposition":[80,163],"architecture,":[81],"specifically":[82],"designed":[83],"solve":[85],"TSF.":[86,108],"To":[87],"end,":[89],"first":[91],"introduce":[92],"Input-Output":[94],"Difference":[95],"(IOD)":[96],"metric":[97],"pseudo-label":[100],"reveal":[102],"data":[104],"imbalance":[105],"phenomenon":[106],"This":[109],"label":[110,127],"continuity":[111],"inherently":[112],"provides":[113],"meaningful":[115],"distance":[116],"between":[117,122],"targets,":[118],"implying":[119],"similarity":[121],"nearby":[123],"targets":[124],"both":[126],"feature":[129,143],"spaces.":[130],"Based":[131],"similarity,":[134],"proposed":[136],"loss":[139],"aims":[140],"reshape":[142],"embeddings":[144],"facilitate":[146],"knowledge":[147],"dissemination":[148],"among":[149],"learn":[153],"specific":[154],"predictive":[155],"features.":[156],"Finally,":[157],"when":[158],"combined":[159],"with":[160],"our":[161],"network,":[164],"iBACon":[165,173],"improves":[167,180],"TSF":[168],"accuracy.":[169],"Experiments":[170],"show":[171],"enhances":[174],"overall":[175],"average":[176],"accuracy":[177],"substantially":[179],"1-3%":[182],"samples.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-10T07:45:09.275182","created_date":"2025-10-10T00:00:00"}
