{"id":"https://openalex.org/W7125971688","doi":"https://doi.org/10.1109/tkde.2026.3658637","title":"Preference Guided Meta-Learning for Cross Domain Time Series Forecasting","display_name":"Preference Guided Meta-Learning for Cross Domain Time Series Forecasting","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125971688","doi":"https://doi.org/10.1109/tkde.2026.3658637"},"language":null,"primary_location":{"id":"doi:10.1109/tkde.2026.3658637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2026.3658637","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/A5101925686","display_name":"Xingwang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xingwang Li","raw_affiliation_strings":["Computing, Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Computing, Artificial Intelligence, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084568567","display_name":"Fei Teng","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Teng","raw_affiliation_strings":["Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124057956","display_name":"Tianrui Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrui Li","raw_affiliation_strings":["Computing, Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Computing, Artificial Intelligence, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121463312","display_name":"Qiang Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]},{"id":"https://openalex.org/I2803030221","display_name":"Abington Memorial Hospital","ror":"https://ror.org/03jgh1p68","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2803030221"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Duan","raw_affiliation_strings":["Information Sciences &#x0026; Technology Department, Pennsylvania State University, Abington, PA, USA"],"affiliations":[{"raw_affiliation_string":"Information Sciences &#x0026; Technology Department, Pennsylvania State University, Abington, PA, USA","institution_ids":["https://openalex.org/I130769515","https://openalex.org/I2803030221"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101925686"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23025349,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"4","first_page":"2366","last_page":"2379"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.25859999656677246,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.25859999656677246,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.23929999768733978,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.17880000174045563,"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/dependency","display_name":"Dependency (UML)","score":0.7483999729156494},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6717000007629395},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5580000281333923},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5042999982833862},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.45089998841285706},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.43380001187324524},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3939000070095062},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3894999921321869}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8166000247001648},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.7483999729156494},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6717000007629395},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.519599974155426},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49790000915527344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48260000348091125},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.38280001282691956},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2026.3658637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2026.3658637","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1975684011","https://openalex.org/W2604847698","https://openalex.org/W3177318507","https://openalex.org/W3207999419","https://openalex.org/W4213433938","https://openalex.org/W4382203079","https://openalex.org/W4385064145","https://openalex.org/W4389987687","https://openalex.org/W4391164176","https://openalex.org/W4391388021","https://openalex.org/W4394699135","https://openalex.org/W4396758709","https://openalex.org/W4396877909","https://openalex.org/W4400039945","https://openalex.org/W4400910435","https://openalex.org/W4400910456","https://openalex.org/W4401109506","https://openalex.org/W4401326695","https://openalex.org/W4402670000","https://openalex.org/W4402671654","https://openalex.org/W4402702977","https://openalex.org/W4402727363","https://openalex.org/W4402727405","https://openalex.org/W4403182907","https://openalex.org/W4403600951","https://openalex.org/W4404035325","https://openalex.org/W4404101524","https://openalex.org/W4404371571","https://openalex.org/W4404782689","https://openalex.org/W4404783041","https://openalex.org/W4411048627","https://openalex.org/W4415797141","https://openalex.org/W7133218509"],"related_works":[],"abstract_inverted_index":{"Time":[0],"series":[1,16,50],"forecasting":[2,27,157],"has":[3],"become":[4],"a":[5,103,116],"critical":[6],"task":[7],"in":[8,128],"data":[9,17],"engineering,":[10],"with":[11],"the":[12,96],"volume":[13],"of":[14],"time":[15,49,131],"projected":[18],"to":[19,32,91,119,135],"reach":[20],"180":[21],"ZB":[22],"by":[23],"2025.":[24],"While":[25],"traditional":[26],"models":[28,126],"are":[29],"typically":[30],"constrained":[31],"single":[33],"domains,":[34,53,140],"missing":[35],"opportunities":[36],"for":[37],"transferring":[38],"temporal":[39,64,111],"patterns":[40],"across":[41,138],"different":[42,52,139],"domains.":[43],"Through":[44,145],"analysis,":[45],"we":[46,149],"observe":[47],"that":[48,106,152],"from":[51,95],"despite":[54],"their":[55],"distinct":[56],"statistical":[57],"characteristics,":[58],"can":[59],"be":[60,171],"fundamentally":[61],"understood":[62],"through":[63,110],"dependency":[65,97,112],"patterns,":[66],"which":[67],"manifest":[68],"as":[69],"either":[70],"long-term":[71],"dependencies":[72,80],"(":[73,81],"like":[74,82],"trends":[75],"and":[76,84,114,132],"cycles)":[77],"or":[78],"short-term":[79],"fluctuations":[83],"abrupt":[85],"changes).":[86],"This":[87],"observation":[88],"motivates":[89],"us":[90],"rethink":[92],"cross-domain":[93,108,121,143],"modeling":[94],"preferences":[98,113],"perspective.":[99],"We":[100],"propose":[101],"LSTPO,":[102],"novel":[104],"framework":[105],"captures":[107],"commonalities":[109],"leverages":[115],"meta-learning-based":[117],"approach":[118],"prevent":[120],"training":[122],"forgetting.":[123],"LSTPO":[124,153],"dynamically":[125],"changes":[127],"preference":[129,136],"over":[130],"swiftly":[133],"adapts":[134],"variations":[137],"enabling":[141],"robust":[142],"forecasting.":[144],"extensive":[146],"experimental":[147],"evaluations,":[148],"have":[150],"shown":[151],"substantially":[154],"outperforms":[155],"state-of-the-art":[156],"methods":[158],"while":[159],"enhancing":[160],"model":[161],"transferability":[162],"under":[163],"few-shot":[164],"learning":[165],"conditions.":[166],"The":[167],"source":[168],"code":[169],"will":[170],"made":[172],"publicly":[173],"available":[174],"upon":[175],"acceptance.":[176]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-01-29T00:00:00"}
