{"id":"https://openalex.org/W4399758752","doi":"https://doi.org/10.48550/arxiv.2406.09643","title":"Reinforced Decoder: Towards Training Recurrent Neural Networks for Time\n Series Forecasting","display_name":"Reinforced Decoder: Towards Training Recurrent Neural Networks for Time\n Series Forecasting","publication_year":2024,"publication_date":"2024-06-13","ids":{"openalex":"https://openalex.org/W4399758752","doi":"https://doi.org/10.48550/arxiv.2406.09643"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.09643","pdf_url":"https://arxiv.org/pdf/2406.09643","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2406.09643","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033812635","display_name":"Qi Sima","orcid":"https://orcid.org/0009-0004-6585-0759"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sima, Qi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054237225","display_name":"Xinze Zhang","orcid":"https://orcid.org/0009-0004-2700-8413"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xinze","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027578915","display_name":"Yukun Bao","orcid":"https://orcid.org/0000-0001-5418-8799"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao, Yukun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102649425","display_name":"Siyue Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Siyue","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090531367","display_name":"Liang Shen","orcid":"https://orcid.org/0000-0002-1818-1271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Liang","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":85},"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":"Clustering of Time Series Data and Algorithms","score":0.9867,"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":"Clustering of Time Series Data and Algorithms","score":0.9867,"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":"Predicting Stock Market Trends and Movements","score":0.9472,"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/T10320","display_name":"Neural Network Fundamentals and Applications","score":0.9468,"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/recurrent-neural-networks","display_name":"Recurrent Neural Networks","score":0.673061},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"Time Series Forecasting","score":0.657249},{"id":"https://openalex.org/keywords/feedforward-neural-networks","display_name":"Feedforward Neural Networks","score":0.621196},{"id":"https://openalex.org/keywords/backpropagation-learning","display_name":"Backpropagation Learning","score":0.617044},{"id":"https://openalex.org/keywords/forecasting-models","display_name":"Forecasting Models","score":0.6122}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.70603925},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6325774},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5897678},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.57980335},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5737859},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47485828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44367927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38026866},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07800451},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.063150674},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.057456225},{"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":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.09643","pdf_url":"https://arxiv.org/pdf/2406.09643","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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/2406.09643","pdf_url":"https://arxiv.org/pdf/2406.09643","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4298287631","https://openalex.org/W4225394202","https://openalex.org/W3146111732","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W2953061907","https://openalex.org/W2622688551","https://openalex.org/W1990205660","https://openalex.org/W1847088711","https://openalex.org/W1550175370"],"abstract_inverted_index":{"Recurrent":[0],"neural":[1],"network-based":[2],"sequence-to-sequence":[3,136],"models":[4,15,84],"have":[5],"been":[6],"extensively":[7],"applied":[8],"for":[9],"multi-step-ahead":[10],"time":[11],"series":[12],"forecasting.":[13],"These":[14],"typically":[16],"involve":[17],"a":[18,74,96],"decoder":[19,33,88],"trained":[20],"using":[21,52],"either":[22],"its":[23],"previous":[24],"forecasts":[25],"or":[26],"the":[27,32,43,53,65,105,125],"actual":[28,54],"observed":[29],"values":[30,61],"as":[31,59],"inputs.":[34],"However,":[35],"relying":[36],"on":[37],"self-generated":[38],"predictions":[39],"can":[40],"lead":[41],"to":[42,85,102,108,134],"rapid":[44],"accumulation":[45],"of":[46],"errors":[47],"over":[48,121],"multiple":[49],"steps,":[50],"while":[51],"observations":[55],"introduces":[56,82],"exposure":[57],"bias":[58],"these":[60],"are":[62],"unavailable":[63],"during":[64],"extrapolation":[66],"stage.":[67],"In":[68],"this":[69,71],"regard,":[70],"study":[72],"proposes":[73],"novel":[75],"training":[76,119],"approach":[77,116,127],"called":[78],"reinforced":[79],"decoder,":[80],"which":[81],"auxiliary":[83],"generate":[86],"alternative":[87],"inputs":[89,107],"that":[90,114],"remain":[91],"accessible":[92],"when":[93,132],"extrapolating.":[94],"Additionally,":[95],"reinforcement":[97],"learning":[98],"algorithm":[99],"is":[100],"utilized":[101],"dynamically":[103],"select":[104],"optimal":[106],"improve":[109],"accuracy.":[110],"Comprehensive":[111],"experiments":[112],"demonstrate":[113],"our":[115],"outperforms":[117],"representative":[118],"methods":[120],"several":[122],"datasets.":[123],"Furthermore,":[124],"proposed":[126],"also":[128],"exhibits":[129],"promising":[130],"performance":[131],"generalized":[133],"self-attention-based":[135],"forecasting":[137],"models.":[138]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4399758752","counts_by_year":[],"updated_date":"2024-11-22T03:25:45.431735","created_date":"2024-06-18"}