{"id":"https://openalex.org/W4398192589","doi":"https://doi.org/10.48550/arxiv.2405.12038","title":"Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven","display_name":"Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven","publication_year":2024,"publication_date":"2024-05-20","ids":{"openalex":"https://openalex.org/W4398192589","doi":"https://doi.org/10.48550/arxiv.2405.12038"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2405.12038","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.12038","pdf_url":"https://arxiv.org/pdf/2405.12038","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.12038","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101602143","display_name":"Dandan Zhang","orcid":"https://orcid.org/0000-0001-6229-4671","observed_orcids":["https://orcid.org/0000-0001-6229-4671"]},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Dandan","raw_affiliation_strings":["School of Computer Science and Engineering , Southeast University , Nanjing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering , Southeast University , Nanjing , China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Zhiqiang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Zhiqiang","raw_affiliation_strings":["School of Computer Science and Engineering , Southeast University , Nanjing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering , Southeast University , Nanjing , China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Nanguang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chen, Nanguang","raw_affiliation_strings":["College of Design and Engineering , National University of Singapore , Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Design and Engineering , National University of Singapore , Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wang, Yun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang, Yun","raw_affiliation_strings":["School of Computer Science and Engineering , Southeast University , Nanjing , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering , Southeast University , Nanjing , China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9955999851226807,"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":0.9955999851226807,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.002400000113993883,"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":0.0012000000569969416,"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/time-series-forecasting","display_name":"time series forecasting","score":0.9470000267028809},{"id":"https://openalex.org/keywords/multivariate-time-series","display_name":"multivariate time series","score":0.8980000019073486},{"id":"https://openalex.org/keywords/adaptive-convolution","display_name":"adaptive convolution","score":0.7549999952316284},{"id":"https://openalex.org/keywords/deformable-convolution","display_name":"deformable convolution","score":0.9520000219345093},{"id":"https://openalex.org/keywords/nonlinear-feature-extraction","display_name":"nonlinear feature extraction","score":0.7179999947547913},{"id":"https://openalex.org/keywords/temporal-dependencies","display_name":"temporal dependencies","score":0.6340000033378601},{"id":"https://openalex.org/keywords/short-term-forecasting","display_name":"short-term forecasting","score":0.7279999852180481},{"id":"https://openalex.org/keywords/long-term-forecasting","display_name":"long-term forecasting","score":0.9950000047683716}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.792337954044342},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7145558595657349},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6615647077560425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5202332735061646},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4740152955055237},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.44846946001052856},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4353826642036438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3497326970100403},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2529793977737427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24379834532737732},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08212262392044067},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0692782998085022},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.052044451236724854},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2405.12038","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.12038","pdf_url":"https://arxiv.org/pdf/2405.12038","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.12038","doi":"https://doi.org/10.48550/arxiv.2405.12038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.12038","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.12038","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.12038","pdf_url":"https://arxiv.org/pdf/2405.12038","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4398192589.pdf","grobid_xml":"https://content.openalex.org/works/W4398192589.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W1919101720","https://openalex.org/W1570805059","https://openalex.org/W2118640767","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Time":[0],"series":[1,31,40,146],"data":[2],"in":[3,136,160,179,202],"real-world":[4,191],"scenarios":[5],"contain":[6],"a":[7],"substantial":[8],"amount":[9],"of":[10,20,187],"nonlinear":[11,46,105,132,166,173],"information,":[12],"which":[13],"significantly":[14],"interferes":[15],"with":[16,75,209],"the":[17,29,35,44,69,90,99,124,131,161,172,180,185],"training":[18],"process":[19],"models,":[21],"leading":[22],"to":[23,79,102,121],"decreased":[24],"prediction":[25],"performance.":[26],"Therefore,":[27],"during":[28],"time":[30,39,50,108,138,145,162,177,181],"forecasting":[32,207],"process,":[33],"extracting":[34,142],"local":[36,125,154],"and":[37,42,63,87,126,130,143,157,205],"global":[38,127,158],"patterns":[41,159],"understanding":[43],"potential":[45,104],"features":[47,106,133,147,174],"among":[48,107,175],"different":[49,76,85,149,176],"observations":[51,135,178],"are":[52],"highly":[53],"significant.":[54],"To":[55],"address":[56],"this":[57],"challenge,":[58],"we":[59,97,113,151],"introduce":[60],"multi-resolution":[61],"convolution":[62,65,73],"deformable":[64],"operations.":[66],"By":[67],"enlarging":[68],"receptive":[70],"field":[71],"using":[72],"kernels":[74],"dilation":[77],"factors":[78],"capture":[80,103,152],"temporal":[81,128],"correlation":[82],"information":[83,156],"at":[84,148],"resolutions,":[86,150],"adaptively":[88],"adjusting":[89],"sampling":[91],"positions":[92],"through":[93],"additional":[94],"offset":[95],"vectors,":[96],"enhance":[98],"network's":[100],"ability":[101],"observations.":[109],"Building":[110],"upon":[111],"this,":[112],"propose":[114],"ACNet,":[115],"an":[116],"adaptive":[117,168],"convolutional":[118],"network":[119],"designed":[120,165],"effectively":[122],"model":[123],"dependencies":[129],"between":[134],"multivariate":[137],"series.":[139,163,182],"Specifically,":[140],"by":[141],"fusing":[144],"both":[153,203],"contextual":[155],"The":[164,193],"feature":[167],"extraction":[169],"module":[170],"captures":[171],"We":[183],"evaluated":[184],"performance":[186,201],"ACNet":[188,197],"across":[189],"twelve":[190],"datasets.":[192],"results":[194],"indicate":[195],"that":[196],"consistently":[198],"achieves":[199],"state-of-the-art":[200],"short-term":[204],"long-term":[206],"tasks":[208],"favorable":[210],"runtime":[211],"efficiency.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2024-05-22T00:00:00"}
