{"id":"https://openalex.org/W4413940652","doi":"https://doi.org/10.32604/cmc.2025.068024","title":"A Survey of Deep Learning for Time Series Forecasting: Theories, Datasets, and State-of-the-Art Techniques","display_name":"A Survey of Deep Learning for Time Series Forecasting: Theories, Datasets, and State-of-the-Art Techniques","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413940652","doi":"https://doi.org/10.32604/cmc.2025.068024"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.068024","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.068024","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.068024","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055387418","display_name":"Gaoyong Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gaoyong Lu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063203947","display_name":"Yang Ou","orcid":"https://orcid.org/0000-0002-9309-2121"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Ou","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461020","display_name":"Zhihong Wang","orcid":"https://orcid.org/0000-0002-5037-1884"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhihong Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040772796","display_name":"Youyang Qu","orcid":"https://orcid.org/0000-0002-2944-4647"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yingnan Qu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yingsheng Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yingsheng Xia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Dibin Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dibin Tang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033986640","display_name":"Igor Kotenko","orcid":"https://orcid.org/0000-0001-6859-7120"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Igor Kotenko","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5083387302","display_name":"Wei Li","orcid":"https://orcid.org/0009-0001-0131-4955"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5055387418"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.1276,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.97758329,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"85","issue":"2","first_page":"2403","last_page":"2441"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.964900016784668,"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":0.964900016784668,"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":0.9413999915122986,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9043999910354614,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6962653398513794},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5988802313804626},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5414547920227051},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5106363892555237},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4736018180847168},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4553123712539673},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.38861826062202454},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3534482717514038},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22248655557632446},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11326685547828674},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08460032939910889}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6962653398513794},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5988802313804626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5414547920227051},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5106363892555237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4736018180847168},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4553123712539673},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38861826062202454},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3534482717514038},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22248655557632446},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11326685547828674},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08460032939910889},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.068024","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.068024","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.068024","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.068024","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1964940342","https://openalex.org/W1973207880","https://openalex.org/W1982978808","https://openalex.org/W2012079387","https://openalex.org/W2029803196","https://openalex.org/W2035301923","https://openalex.org/W2039240409","https://openalex.org/W2102148524","https://openalex.org/W2110485445","https://openalex.org/W2157331557","https://openalex.org/W2255466643","https://openalex.org/W2296609147","https://openalex.org/W2613328025","https://openalex.org/W2804416042","https://openalex.org/W2905238323","https://openalex.org/W2940914091","https://openalex.org/W2971724044","https://openalex.org/W2991081473","https://openalex.org/W3000499162","https://openalex.org/W3006135925","https://openalex.org/W3017116930","https://openalex.org/W3022367555","https://openalex.org/W3147254695","https://openalex.org/W3171884590","https://openalex.org/W3190469032","https://openalex.org/W3194625338","https://openalex.org/W4280550128","https://openalex.org/W4304893142","https://openalex.org/W4312703862","https://openalex.org/W4366310725","https://openalex.org/W4367299897","https://openalex.org/W4380680516","https://openalex.org/W4381326995","https://openalex.org/W4383752905","https://openalex.org/W4385804011","https://openalex.org/W4386799830","https://openalex.org/W4388430419","https://openalex.org/W4391308936","https://openalex.org/W4391621245","https://openalex.org/W4407230811","https://openalex.org/W4409695029"],"related_works":["https://openalex.org/W1919101720","https://openalex.org/W4390822878","https://openalex.org/W4246257243","https://openalex.org/W96888382","https://openalex.org/W4386126592","https://openalex.org/W2041308758","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Deep":[0],"learning":[1,17],"(DL)":[2],"has":[3],"revolutionized":[4],"time":[5],"series":[6],"forecasting":[7,80],"(TSF),":[8],"surpassing":[9],"traditional":[10],"statistical":[11,175],"methods":[12],"(e.g.,":[13,112,129],"ARIMA)":[14],"and":[15,24,62,87,118,135,154,176,189,206,216,222],"machine":[16],"techniques":[18,178],"in":[19,28,224],"modeling":[20],"complex":[21],"nonlinear":[22],"dynamics":[23],"long-term":[25],"dependencies":[26],"prevalent":[27],"real-world":[29],"temporal":[30,55,97],"data.":[31],"This":[32,194],"comprehensive":[33],"survey":[34],"reviews":[35],"state-of-the-art":[36],"DL":[37,177],"architectures":[38],"for":[39,69,79,152,168,179,186,191,220],"TSF,":[40],"focusing":[41],"on":[42],"four":[43],"core":[44],"paradigms:":[45],"(1)":[46],"Convolutional":[47],"Neural":[48,59,75],"Networks":[49,60,76],"(CNNs),":[50],"adept":[51],"at":[52],"extracting":[53],"localized":[54],"features;":[56],"(2)":[57],"Recurrent":[58],"(RNNs)":[61],"their":[63],"advanced":[64],"variants":[65],"(LSTM,":[66],"GRU),":[67],"designed":[68],"sequential":[70],"dependency":[71],"modeling;":[72],"(3)":[73],"Graph":[74],"(GNNs),":[77],"specialized":[78],"structured":[81],"relational":[82],"data":[83],"with":[84,184],"spatial-temporal":[85],"dependencies;":[86],"(4)":[88],"Transformer-based":[89],"models,":[90],"leveraging":[91],"self-attention":[92],"mechanisms":[93],"to":[94,209],"capture":[95],"global":[96],"patterns":[98],"efficiently.":[99],"We":[100],"provide":[101],"a":[102],"rigorous":[103],"analysis":[104],"of":[105,121],"the":[106,211,225],"theoretical":[107,214],"underpinnings,":[108],"recent":[109],"algorithmic":[110],"advancements":[111],"TCNs,":[113],"attention":[114],"mechanisms,":[115],"hybrid":[116,171],"architectures),":[117],"practical":[119,217],"applications":[120],"each":[122],"framework,":[123],"supported":[124],"by":[125],"extensive":[126],"benchmark":[127],"datasets":[128],"ETT,":[130],"traffic":[131],"flow,":[132],"financial":[133],"indicators)":[134],"standardized":[136],"evaluation":[137],"metrics":[138],"(MAE,":[139],"MSE,":[140],"RMSE).":[141],"Critical":[142],"challenges,":[143],"including":[144],"handling":[145],"irregular":[146],"sampling":[147],"intervals,":[148],"integrating":[149],"domain":[150],"knowledge":[151],"robustness,":[153],"managing":[155],"computational":[156],"complexity,":[157],"are":[158],"thoroughly":[159],"discussed.":[160],"Emerging":[161],"research":[162,215],"directions":[163],"highlighted":[164],"include":[165],"diffusion":[166],"models":[167],"uncertainty":[169],"quantification,":[170],"pipelines":[172],"combining":[173],"classical":[174],"enhanced":[180],"interpretability,":[181],"quantile":[182],"regression":[183],"Transformers":[185],"risk-aware":[187],"forecasting,":[188],"optimizations":[190],"real-time":[192],"deployment.":[193],"work":[195],"serves":[196],"as":[197],"an":[198],"essential":[199],"reference,":[200],"consolidating":[201],"methodological":[202],"innovations,":[203],"empirical":[204],"resources,":[205],"future":[207],"trends":[208],"bridge":[210],"gap":[212],"between":[213],"implementation":[218],"needs":[219],"researchers":[221],"practitioners":[223],"field.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":6}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}