{"id":"https://openalex.org/W7131426811","doi":"https://doi.org/10.1109/icdm65498.2025.00153","title":"Towards Generalizable Time Series Forecasting Via IB-Regularized Transformer-Diffusion","display_name":"Towards Generalizable Time Series Forecasting Via IB-Regularized Transformer-Diffusion","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7131426811","doi":"https://doi.org/10.1109/icdm65498.2025.00153"},"language":null,"primary_location":{"id":"doi:10.1109/icdm65498.2025.00153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","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":"https://openalex.org/A5126791384","display_name":"Dagyeong Na","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dagyeong Na","raw_affiliation_strings":["Chung-Ang University,South Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081609107","display_name":"Jinho Kang","orcid":"https://orcid.org/0009-0002-3263-9776"},"institutions":[{"id":"https://openalex.org/I124633538","display_name":"University of Seoul","ror":"https://ror.org/05en5nh73","country_code":"KR","type":"education","lineage":["https://openalex.org/I124633538"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinho Kang","raw_affiliation_strings":["University of Seoul,South Korea"],"affiliations":[{"raw_affiliation_string":"University of Seoul,South Korea","institution_ids":["https://openalex.org/I124633538"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045083125","display_name":"Junseok Kwon","orcid":"https://orcid.org/0000-0001-9526-7549"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junseok Kwon","raw_affiliation_strings":["Chung-Ang University,South Korea"],"affiliations":[{"raw_affiliation_string":"Chung-Ang University,South Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126791384"],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72014791,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1435","last_page":"1444"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.15870000422000885,"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":0.15870000422000885,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.149399995803833,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.06710000336170197,"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/robustness","display_name":"Robustness (evolution)","score":0.5400999784469604},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5099999904632568},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45179998874664307},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.4413999915122986},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.37619999051094055},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.37059998512268066},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.36419999599456787},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.3621000051498413}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038000226020813},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5400999784469604},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5099999904632568},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5069000124931335},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.492900013923645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4569999873638153},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45179998874664307},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3228999972343445},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.2524999976158142},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm65498.2025.00153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm65498.2025.00153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.4986334443092346}],"awards":[{"id":"https://openalex.org/G1264706951","display_name":null,"funder_award_id":"RS-2024-00509724,RS-2025-02217071","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1984113680","https://openalex.org/W2025720061","https://openalex.org/W2550143307","https://openalex.org/W2551393996","https://openalex.org/W2604847698","https://openalex.org/W2887943696","https://openalex.org/W2904560462","https://openalex.org/W2905306513","https://openalex.org/W2980994438","https://openalex.org/W2996565520","https://openalex.org/W3177318507","https://openalex.org/W3186799840","https://openalex.org/W3204801262","https://openalex.org/W4235213111"],"related_works":[],"abstract_inverted_index":{"Multivariate":[0],"Time":[1],"Series":[2],"Forecasting":[3],"(MTSF)":[4],"remains":[5],"challenging":[6],"due":[7],"to":[8,11,41,93],"the":[9,70,86,90,99,135],"need":[10],"capture":[12],"complex":[13],"temporal":[14],"dependencies":[15],"and":[16,30,62,115,129,147],"ensure":[17],"robustness":[18],"against":[19],"distribution":[20],"shift.":[21],"While":[22],"Transformer-based":[23],"models":[24,32,144],"excel":[25],"at":[26],"capturing":[27],"long-range":[28],"patterns,":[29],"diffusion":[31,38,143],"offer":[33],"generative":[34],"flexibility,":[35],"directly":[36],"applying":[37],"often":[39],"leads":[40],"excessive":[42],"uncertainty.":[43],"To":[44,75],"address":[45],"these":[46],"limitations,":[47],"we":[48,78],"propose":[49],"a":[50,80],"unified":[51],"forecasting":[52],"framework":[53],"(IB-TransDiff)":[54],"that":[55,84,106],"combines":[56],"global":[57],"modeling":[58],"based":[59,65],"on":[60,66,102],"Transformers":[61],"local":[63],"refinement":[64],"diffusion,":[67],"guided":[68],"by":[69],"Information":[71],"Bottleneck":[72],"(IB)":[73],"principle.":[74],"improve":[76],"generalization,":[77],"introduce":[79],"novel":[81],"regularization":[82],"strategy":[83],"penalizes":[85],"second":[87],"moment":[88],"of":[89,137],"conditional":[91,142],"embedding":[92],"reduce":[94],"its":[95],"mutual":[96],"information":[97],"with":[98],"input.":[100],"Experiments":[101],"multiple":[103],"benchmarks":[104],"demonstrate":[105],"our":[107,119],"method":[108,120],"consistently":[109],"outperforms":[110],"existing":[111],"approaches":[112],"in":[113,126],"MSE":[114],"MAE.":[116],"In":[117],"addition,":[118],"reduces":[121],"uncertainty,":[122],"achieving":[123],"notable":[124],"improvements":[125],"QICE,":[127],"CRPS,":[128],"PICP":[130],"metrics.":[131],"These":[132],"results":[133],"highlight":[134],"value":[136],"integrating":[138],"information-theoretic":[139],"constraints":[140],"into":[141],"for":[145],"robust":[146],"accurate":[148],"time":[149],"series":[150],"forecasting.":[151]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-02-26T00:00:00"}