{"id":"https://openalex.org/W4285334102","doi":"https://doi.org/10.1109/la-cci48322.2021.9769836","title":"Probabilistic Multistep Time Series Forecasting Using Conditional Generative Adversarial Networks","display_name":"Probabilistic Multistep Time Series Forecasting Using Conditional Generative Adversarial Networks","publication_year":2021,"publication_date":"2021-11-02","ids":{"openalex":"https://openalex.org/W4285334102","doi":"https://doi.org/10.1109/la-cci48322.2021.9769836"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci48322.2021.9769836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci48322.2021.9769836","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","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/A5114024348","display_name":"Gerardo Zuniga","orcid":null},"institutions":[{"id":"https://openalex.org/I10457146","display_name":"Universidad de Santiago de Chile","ror":"https://ror.org/02ma57s91","country_code":"CL","type":"education","lineage":["https://openalex.org/I10457146"]}],"countries":["CL"],"is_corresponding":true,"raw_author_name":"Gerardo Zuniga","raw_affiliation_strings":["Universidad de Santiago de Chile,Departamento de Ingenier&#x00ED;a Inform&#x00E1;tica,Santiago,Chile"],"affiliations":[{"raw_affiliation_string":"Universidad de Santiago de Chile,Departamento de Ingenier&#x00ED;a Inform&#x00E1;tica,Santiago,Chile","institution_ids":["https://openalex.org/I10457146"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112837607","display_name":"Gonzalo Acu\u00f1a","orcid":null},"institutions":[{"id":"https://openalex.org/I10457146","display_name":"Universidad de Santiago de Chile","ror":"https://ror.org/02ma57s91","country_code":"CL","type":"education","lineage":["https://openalex.org/I10457146"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Gonzalo Acuna","raw_affiliation_strings":["Universidad de Santiago de Chile,Departamento de Ingenier&#x00ED;a Inform&#x00E1;tica,Santiago,Chile"],"affiliations":[{"raw_affiliation_string":"Universidad de Santiago de Chile,Departamento de Ingenier&#x00ED;a Inform&#x00E1;tica,Santiago,Chile","institution_ids":["https://openalex.org/I10457146"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114024348"],"corresponding_institution_ids":["https://openalex.org/I10457146"],"apc_list":null,"apc_paid":null,"fwci":0.5228,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70276944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"29","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9977999925613403,"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":0.9977999925613403,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9966999888420105,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9797999858856201,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.763302743434906},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7155563831329346},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6936612129211426},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6077057123184204},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5331800580024719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5287594199180603},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5267433524131775},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5026919841766357},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.44724929332733154},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.4180777668952942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.763302743434906},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7155563831329346},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6936612129211426},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6077057123184204},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5331800580024719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5287594199180603},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5267433524131775},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5026919841766357},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.44724929332733154},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.4180777668952942},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/la-cci48322.2021.9769836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci48322.2021.9769836","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","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":15,"referenced_works":["https://openalex.org/W1996102080","https://openalex.org/W2109248217","https://openalex.org/W2577946330","https://openalex.org/W2602977295","https://openalex.org/W2796652797","https://openalex.org/W2796929742","https://openalex.org/W2816609771","https://openalex.org/W2902545569","https://openalex.org/W2912614123","https://openalex.org/W2930586974","https://openalex.org/W4301206121","https://openalex.org/W4320013936","https://openalex.org/W6649061795","https://openalex.org/W6732248266","https://openalex.org/W6765779288"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W4320018150","https://openalex.org/W2040808657","https://openalex.org/W4239582170","https://openalex.org/W2918664383","https://openalex.org/W106056076","https://openalex.org/W4320855730","https://openalex.org/W2135200719"],"abstract_inverted_index":{"Time":[0],"series":[1,71,86],"forecasting":[2,57],"is":[3,30,41,44,110,151],"a":[4,45,81,102,125],"problem":[5,34],"that":[6,80,128],"has":[7,77,92],"been":[8,78,93],"studied":[9],"for":[10,55,96],"many":[11],"years":[12],"due":[13,35],"to":[14,36,47,64,114],"the":[15,21,37,136,144,147],"impact":[16],"it":[17],"can":[18],"have":[19,65],"on":[20],"world":[22],"economy":[23],"and":[24,49,52,157],"well-being.":[25],"Predicting":[26],"multiple":[27,108],"future":[28],"values":[29],"an":[31,66],"especially":[32],"complex":[33],"increasing":[38],"error.":[39],"This":[40],"why":[42],"there":[43],"need":[46],"design":[48],"evaluate":[50],"more":[51],"better":[53],"methods":[54],"this":[56,100],"problem.":[58],"The":[59],"adversarial":[60],"generative":[61,90],"networks":[62],"seem":[63],"excellent":[67],"performance":[68],"generating":[69],"time":[70,85],"indistinguishable":[72],"from":[73],"real":[74,126],"series.":[75],"It":[76],"shown":[79],"probabilistic":[82],"prediction":[83],"of":[84,105,122,132,140,146],"called":[87],"ForGAN":[88,106,141,149],"adversary":[89],"network":[91,150],"successfully":[94],"used":[95],"one-step-ahead":[97],"predictions.":[98,117],"In":[99],"work,":[101],"modified":[103,138],"architecture":[104,139],"with":[107,135,143],"outputs":[109],"proposed":[111,137],"in":[112],"order":[113],"perform":[115],"multiple-step-ahead":[116,133],"We":[118],"show":[119],"by":[120,155,159],"means":[121],"experiments":[123],"using":[124],"dataset":[127],"statistically":[129],"significant":[130],"improvement":[131],"predictions":[134],"compared":[142],"use":[145],"original":[148],"achieved,":[152],"decreasing":[153],"RMSE":[154],"17.6%":[156],"CRPS":[158],"17.3%":[160],"when":[161],"predicting":[162],"5":[163],"steps":[164],"ahead.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
