{"id":"https://openalex.org/W4416250955","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227806","title":"TF-DPNet: A Time-Frequency with Dual Perspectives Network for Multivariate Time Series Prediction","display_name":"TF-DPNet: A Time-Frequency with Dual Perspectives Network for Multivariate Time Series Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250955","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227806"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227806","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/A5100653699","display_name":"Zhijiang Wang","orcid":"https://orcid.org/0000-0001-9314-7922"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhijiang Wang","raw_affiliation_strings":["Yunnan University,School of Information Science & Engineering,Kunming,China"],"affiliations":[{"raw_affiliation_string":"Yunnan University,School of Information Science & Engineering,Kunming,China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5118983705","display_name":"Yaoyuan Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoyuan Yang","raw_affiliation_strings":["Yunnan University,School of Information Science & Engineering,Kunming,China"],"affiliations":[{"raw_affiliation_string":"Yunnan University,School of Information Science & Engineering,Kunming,China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100653699"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41913874,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.32820001244544983,"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":0.32820001244544983,"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":0.188400000333786,"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":0.1809999942779541,"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":0.6970999836921692},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.6301000118255615},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6251000165939331},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6019999980926514},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5601000189781189},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48240000009536743},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.46380001306533813},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.45840001106262207}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6970999836921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686000227928162},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.6301000118255615},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6251000165939331},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6019999980926514},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5882999897003174},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5601000189781189},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4731999933719635},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.44679999351501465},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.3953000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3921999931335449},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227806","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":28,"referenced_works":["https://openalex.org/W1993512681","https://openalex.org/W2036785686","https://openalex.org/W2063433532","https://openalex.org/W2134383774","https://openalex.org/W2604847698","https://openalex.org/W2607162077","https://openalex.org/W2962790412","https://openalex.org/W2962834855","https://openalex.org/W2997848713","https://openalex.org/W3130456109","https://openalex.org/W3177318507","https://openalex.org/W4379382937","https://openalex.org/W4382203079","https://openalex.org/W4392902965","https://openalex.org/W4392904353","https://openalex.org/W4393145119","https://openalex.org/W4396680645","https://openalex.org/W4399657382","https://openalex.org/W4399993796","https://openalex.org/W4400490325","https://openalex.org/W4402980042","https://openalex.org/W4405718500","https://openalex.org/W4407026819","https://openalex.org/W4408352983","https://openalex.org/W4408353440","https://openalex.org/W4408803960","https://openalex.org/W4411631879","https://openalex.org/W4415797384"],"related_works":[],"abstract_inverted_index":{"Multivariate":[0],"time":[1,28,38,67,75,85,107,116,121],"series":[2,29,68,86],"prediction":[3,47,142],"is":[4,146],"widely":[5],"applied":[6],"across":[7],"various":[8],"fields.":[9],"Recently,":[10],"MLP-based":[11],"forecasting":[12,153],"methods":[13,25],"have":[14],"shown":[15],"potential":[16],"due":[17],"to":[18,41,91,110,140],"their":[19],"efficiency.":[20,157],"However,":[21],"most":[22],"of":[23,84],"these":[24,98],"analyze":[26],"complex":[27],"data":[30,87],"primarily":[31],"from":[32,129],"a":[33,56,115,137],"global":[34,80],"perspective":[35],"in":[36],"the":[37,79,126],"domain,":[39],"leading":[40],"incomplete":[42],"feature":[43,117,123,138],"extraction":[44],"and":[45,74,81,105,134,155],"affecting":[46],"accuracy.":[48],"To":[49],"address":[50],"this":[51],"issue,":[52],"we":[53],"propose":[54],"TF-DPNet,":[55],"lightweight":[57],"Time":[58],"Frequency":[59],"Integrated":[60],"Network":[61],"with":[62],"Dual":[63],"Perspectives":[64],"for":[65],"multivariate":[66],"prediction.":[69],"TF-DPNet":[70,96],"leverages":[71],"both":[72],"frequency":[73,102],"domain":[76,103,108,122],"information,":[77],"treating":[78],"local":[82],"perspectives":[83,131],"as":[88],"separate":[89],"inputs":[90,99],"enhance":[92],"predictive":[93],"capability.":[94],"Specifically,":[95],"processes":[97],"by":[100],"extending":[101],"information":[104],"reconstructing":[106],"features":[109,127],"capture":[111],"rich":[112],"characteristics.":[113],"Subsequently,":[114],"extractor":[118],"further":[119],"enhances":[120],"extraction.":[124],"Finally,":[125],"extracted":[128],"dual":[130],"are":[132],"integrated":[133],"optimized":[135],"using":[136],"amplifier":[139],"improve":[141],"performance.":[143],"TF-DPNet\u2019s":[144],"performance":[145],"validated":[147],"on":[148],"real-world":[149],"datasets,":[150],"showing":[151],"superior":[152],"accuracy":[154],"computational":[156]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}