乐胖代购免代理版

{"id":"https://openalex.org/W2604847698","doi":"https://doi.org/10.1145/3209978.3210006","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","display_name":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","publication_year":2018,"publication_date":"2018-06-27","ids":{"openalex":"https://openalex.org/W2604847698","doi":"https://doi.org/10.1145/3209978.3210006","mag":"2604847698"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209978.3210006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1703.07015","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015579277","display_name":"Guokun Lai","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"funder","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guokun Lai","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006559148","display_name":"Wei-Cheng Chang","orcid":"https://orcid.org/0000-0002-5646-9356"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"funder","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-Cheng Chang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106542734","display_name":"Yiming Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"funder","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiming Yang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085154966","display_name":"Hanxiao Liu","orcid":"https://orcid.org/0000-0003-3106-7542"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"funder","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanxiao Liu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":1442,"citation_normalized_percentile":{"value":0.999128,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994,"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":0.9994,"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":0.9982,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.991,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.76245844}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.76245844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7448273},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7198418},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.71762025},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6273829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6113647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.56325245},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5492877},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5168262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.51578903},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.49629313},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4921226},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4590849},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4144741},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33397228},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2665918},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.16715199},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209978.3210006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1703.07015","pdf_url":"https://arxiv.org/pdf/1703.07015","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1703.07015","pdf_url":"https://arxiv.org/pdf/1703.07015","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":35,"referenced_works":["https://openalex.org/W1586335931","https://openalex.org/W1924770834","https://openalex.org/W1949741824","https://openalex.org/W2003602843","https://openalex.org/W2012079387","https://openalex.org/W2016287239","https://openalex.org/W2064675550","https://openalex.org/W2080690725","https://openalex.org/W2095654324","https://openalex.org/W2110485445","https://openalex.org/W2117014758","https://openalex.org/W2123737232","https://openalex.org/W2133564696","https://openalex.org/W2141173017","https://openalex.org/W2160815625","https://openalex.org/W2168431040","https://openalex.org/W2172073485","https://openalex.org/W2288074780","https://openalex.org/W2313953460","https://openalex.org/W2510642588","https://openalex.org/W2552480641","https://openalex.org/W2559655401","https://openalex.org/W2604272474","https://openalex.org/W2618530766","https://openalex.org/W2624190409","https://openalex.org/W2739805805","https://openalex.org/W2798058877","https://openalex.org/W2947626232","https://openalex.org/W2951305674","https://openalex.org/W2952680595","https://openalex.org/W3035805403","https://openalex.org/W3122503089","https://openalex.org/W3146166473","https://openalex.org/W4241115065","https://openalex.org/W4247950230"],"related_works":["https://openalex.org/W4298287631","https://openalex.org/W4225394202","https://openalex.org/W3036642985","https://openalex.org/W3034302643","https://openalex.org/W3032952384","https://openalex.org/W2964335273","https://openalex.org/W2953061907","https://openalex.org/W2229372569","https://openalex.org/W1889624880","https://openalex.org/W1847088711"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,104],"series":[2,105],"forecasting":[3],"is":[4],"an":[5],"important":[6],"machine":[7],"learning":[8,63],"problem":[9,118],"across":[10],"many":[11],"domains,":[12],"including":[13],"predictions":[14],"of":[15,37,119,133,143],"solar":[16],"plant":[17],"energy":[18],"output,":[19],"electricity":[20],"consumption,":[21],"and":[22,39,50,67,84,98,151],"traffic":[23],"jam":[24],"situation.":[25],"Temporal":[26],"data":[27,129,150],"arise":[28],"in":[29],"these":[30],"real-world":[31,128],"applications":[32],"often":[33],"involves":[34],"a":[35,60],"mixture":[36],"long-term":[38,101],"short-term":[40,92],"patterns,":[41,135],"for":[42,103],"which":[43],"traditional":[44,110],"approaches":[45],"such":[46],"as":[47],"Autoregressive":[48],"models":[49],"Gaussian":[51],"Process":[52],"may":[53],"fail.":[54],"In":[55,124],"this":[56,74],"paper,":[57],"we":[58,108],"proposed":[59],"novel":[61],"deep":[62],"framework,":[64],"namely":[65],"Long-":[66],"Short-term":[68],"Time-series":[69],"network":[70,122],"(LSTNet),":[71],"to":[72,90,99,113],"address":[73],"open":[75],"challenge.":[76],"LSTNet":[77,136],"uses":[78],"the":[79,85,115,120,149],"Convolution":[80],"Neural":[81,87],"Network":[82,88],"(CNN)":[83],"Recurrent":[86],"(RNN)":[89],"extract":[91],"local":[93],"dependency":[94],"patterns":[95,102],"among":[96],"variables":[97],"discover":[100],"trends.":[106],"Furthermore,":[107],"leverage":[109],"autoregressive":[111],"model":[112],"tackle":[114],"scale":[116],"insensitive":[117],"neural":[121],"model.":[123],"our":[125],"evaluation":[126],"on":[127],"with":[130],"complex":[131],"mixtures":[132],"repetitive":[134],"achieved":[137],"significant":[138],"performance":[139],"improvements":[140],"over":[141],"that":[142],"several":[144],"state-of-the-art":[145],"baseline":[146],"methods.":[147],"All":[148],"experiment":[152],"codes":[153],"are":[154],"available":[155],"online.":[156]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2604847698","counts_by_year":[{"year":2025,"cited_by_count":46},{"year":2024,"cited_by_count":393},{"year":2023,"cited_by_count":347},{"year":2022,"cited_by_count":223},{"year":2021,"cited_by_count":229},{"year":2020,"cited_by_count":129},{"year":2019,"cited_by_count":60},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2025-03-25T12:37:00.161475","created_date":"2017-04-14"}