{"id":"https://openalex.org/W4416251375","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228920","title":"Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism","display_name":"Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251375","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228920"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228920","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228920","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5014523958","display_name":"Yuqi Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuqi Xiong","raw_affiliation_strings":["Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072206345","display_name":"Wen Yang","orcid":"https://orcid.org/0000-0001-8769-7370"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Wen","raw_affiliation_strings":["Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014523958"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":2.3567,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91881769,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.4999000132083893,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.4999000132083893,"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":0.20149999856948853,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.08829999715089798,"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/time-series","display_name":"Time series","score":0.7085000276565552},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6373999714851379},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.6001999974250793},{"id":"https://openalex.org/keywords/fourier-series","display_name":"Fourier series","score":0.4790000021457672},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.39719998836517334},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.3734000027179718},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37119999527931213}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7085000276565552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6689000129699707},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6373999714851379},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C207864730","wikidata":"https://www.wikidata.org/wiki/Q179467","display_name":"Fourier series","level":2,"score":0.4790000021457672},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45989999175071716},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4302999973297119},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39660000801086426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37450000643730164},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C207821765","wikidata":"https://www.wikidata.org/wiki/Q405372","display_name":"Instability","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.29159998893737793},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.25920000672340393},{"id":"https://openalex.org/C177454536","wikidata":"https://www.wikidata.org/wiki/Q578290","display_name":"Emphasis (telecommunications)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228920","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228920","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2164117960","https://openalex.org/W2604847698","https://openalex.org/W2972818416","https://openalex.org/W3162090017","https://openalex.org/W3177318507","https://openalex.org/W3199258042","https://openalex.org/W3207999419","https://openalex.org/W4206706211","https://openalex.org/W4221145213","https://openalex.org/W4242321689","https://openalex.org/W4382203079","https://openalex.org/W4382239131","https://openalex.org/W4385763767","https://openalex.org/W4415795912"],"related_works":[],"abstract_inverted_index":{"Time":[0],"series":[1,25,158],"forecasting":[2,142,154],"has":[3],"important":[4],"applications":[5],"in":[6,21,83,124],"financial":[7],"analysis,":[8],"weather":[9],"forecasting,":[10],"and":[11,58,67,80,102,110,130,139,153,160],"traffic":[12],"management.":[13],"However,":[14],"existing":[15],"deep":[16,166],"learning":[17,167],"models":[18,123],"are":[19],"limited":[20],"processing":[22],"non-stationary":[23,136,156],"time":[24,157,170],"data":[26,137],"because":[27],"they":[28],"cannot":[29],"effectively":[30],"capture":[31],"the":[32,52,77,108,120,151,163],"statistical":[33],"characteristics":[34,82],"that":[35,94,117],"change":[36],"over":[37],"time.":[38],"To":[39],"address":[40],"this":[41,43],"problem,":[42],"paper":[44,145],"proposes":[45],"a":[46,63,90],"new":[47,91],"framework,":[48],"AEFIN,":[49],"which":[50],"enhances":[51],"information":[53],"sharing":[54],"ability":[55],"between":[56],"stable":[57],"unstable":[59,84],"components":[60],"by":[61],"introducing":[62],"cross":[64],"attention":[65],"mechanism,":[66],"combines":[68,95],"Fourier":[69],"analysis":[70],"network":[71],"with":[72],"MLP":[73],"to":[74,106,162],"deeply":[75],"explore":[76],"seasonal":[78],"patterns":[79],"trend":[81],"components.":[85],"In":[86],"addition,":[87],"we":[88],"design":[89],"loss":[92],"function":[93],"time-domain":[96,99],"stability":[97,104],"constraints,":[98,101],"instability":[100],"frequency-domain":[103],"constraints":[105],"improve":[107],"accuracy":[109],"robustness":[111],"of":[112,126,155,165],"forecasting.":[113],"Experimental":[114],"results":[115],"show":[116],"AEFIN":[118],"outperforms":[119],"most":[121],"common":[122],"terms":[125],"mean":[127,131],"square":[128],"error":[129],"absolute":[132],"error,":[133],"especially":[134],"under":[135],"conditions,":[138],"shows":[140],"excellent":[141],"capabilities.":[143],"This":[144],"provides":[146],"an":[147],"innovative":[148],"solution":[149],"for":[150,168],"modeling":[152],"data,":[159],"contributes":[161],"research":[164],"complex":[169],"series.":[171],"Our":[172],"code":[173],"is":[174],"publicly":[175],"available":[176],"at":[177],"https://github.com/YukiBear426/AEFIN.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
