{"id":"https://openalex.org/W7150752365","doi":"https://doi.org/10.1109/access.2026.3680916","title":"Gated-Attention U-Net for Time Series Forecasting: Enhancing Long-term Dependencies with Adaptive Feature Fusion","display_name":"Gated-Attention U-Net for Time Series Forecasting: Enhancing Long-term Dependencies with Adaptive Feature Fusion","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7150752365","doi":"https://doi.org/10.1109/access.2026.3680916"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3680916","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3680916","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3680916","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133014877","display_name":"Xin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I118803816","display_name":"Liaoning University","ror":"https://ror.org/03xpwj629","country_code":"CN","type":"education","lineage":["https://openalex.org/I118803816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Information Center, Liaoning University, Shenyang, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Center, Liaoning University, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I118803816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133017989","display_name":"JiaoJiao Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I91656880","display_name":"China Medical University","ror":"https://ror.org/032d4f246","country_code":"CN","type":"education","lineage":["https://openalex.org/I91656880"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JiaoJiao Cui","raw_affiliation_strings":["Network Information Center, China Medical University, Shenyang, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Network Information Center, China Medical University, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I91656880"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35296443,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.2838999927043915,"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":0.2838999927043915,"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":0.2371000051498413,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.19910000264644623,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.52920001745224},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5026999711990356},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.40630000829696655},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4032999873161316},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3619000017642975}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7400000095367432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303000211715698},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3240000009536743},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3680916","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3680916","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:336a5922d0074bf8afbf769da9777a56","is_oa":true,"landing_page_url":"https://doaj.org/article/336a5922d0074bf8afbf769da9777a56","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 54922-54936 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3680916","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3680916","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.429493248462677}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Time":[0],"series":[1,59],"forecasting":[2,143],"is":[3,200],"a":[4,54,102,193],"critical":[5],"task":[6],"in":[7,29,75,132,196,211],"machine":[8],"learning":[9],"with":[10,192],"broad":[11],"applications":[12],"across":[13],"domains":[14],"such":[15],"as":[16],"transportation,":[17],"energy,":[18],"meteorology,":[19],"and":[20,40,79,112,157,163,207],"finance.":[21],"While":[22],"Transformer-based":[23],"models":[24],"have":[25],"achieved":[26],"remarkable":[27],"success":[28],"deep":[30],"learning,":[31],"they":[32],"are":[33],"often":[34],"hindered":[35],"by":[36,52,202],"high":[37],"computational":[38],"complexity":[39,214],"slow":[41],"inference":[42],"speeds.":[43],"The":[44,129],"recently":[45],"proposed":[46,180],"UnetTSF":[47,72],"model":[48,181],"addresses":[49],"these":[50,137],"issues":[51],"introducing":[53],"U-Net":[55,89,142],"architecture":[56,93],"into":[57],"time":[58,146],"forecasting,":[60],"achieving":[61],"linear":[62],"complexity.":[63],"However,":[64],"due":[65],"to":[66,160,213],"its":[67],"simple":[68],"concatenation-based":[69],"fusion":[70,117],"strategy,":[71],"remains":[73],"limited":[74],"capturing":[76],"long-term":[77,109],"dependencies":[78],"effectively":[80],"integrating":[81],"multi-scale":[82],"features.":[83],"This":[84],"paper":[85],"proposes":[86],"Gated":[87],"Attention":[88],"(UnetTSF-GA),":[90],"an":[91,114],"enhanced":[92],"that":[94,119,173],"incorporates":[95],"two":[96,138],"key":[97],"innovations":[98],"upon":[99],"UnetTSF:":[100],"(1)":[101],"channel-wise":[103],"self-attention":[104],"mechanism":[105,151],"for":[106,145],"explicitly":[107],"modeling":[108],"temporal":[110],"dependencies;":[111],"(2)":[113],"adaptive":[115],"gated":[116],"strategy":[118],"learns":[120],"optimal":[121],"feature":[122],"integration":[123,135],"weights":[124],"while":[125],"preserving":[126],"dimensional":[127],"consistency.":[128],"novelty":[130],"lies":[131],"the":[133,141,176,179,183],"joint":[134],"of":[136],"mechanisms":[139],"within":[140],"pipeline":[144],"series,":[147],"rather":[148],"than":[149],"either":[150],"alone.":[152],"Additionally,":[153],"we":[154],"employ":[155],"LayerNorm":[156],"ReLU":[158],"activation":[159],"stabilize":[161],"training":[162],"improve":[164],"expressive":[165],"capacity.":[166],"Experiments":[167],"on":[168,188],"eight":[169],"benchmark":[170],"datasets":[171],"show":[172],"UnetTSF-GA":[174],"outperforms":[175],"original":[177],"UnetTSF;":[178],"obtains":[182],"best":[184],"MSE":[185],"or":[186],"MAE":[187],"most":[189],"test":[190],"cases":[191],"clear":[194],"reduction":[195],"average":[197],"error.":[198],"Efficiency":[199],"supported":[201],"parameter":[203],"count,":[204],"memory":[205],"usage,":[206],"empirical":[208],"runtime":[209],"benchmarks,":[210],"addition":[212],"analysis.":[215]},"counts_by_year":[],"updated_date":"2026-04-08T06:01:36.053099","created_date":"2026-04-07T00:00:00"}
