{"id":"https://openalex.org/W4318186082","doi":"https://doi.org/10.1109/bigdata55660.2022.10020471","title":"A Hybrid Model Based on NeuralProphet and Long Short-Term Memory for Time Series Forecasting","display_name":"A Hybrid Model Based on NeuralProphet and Long Short-Term Memory for Time Series Forecasting","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318186082","doi":"https://doi.org/10.1109/bigdata55660.2022.10020471"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020471","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5090467881","display_name":"Zhaofeng Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhaofeng Yu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009353311","display_name":"Kun Niu","orcid":"https://orcid.org/0000-0003-1877-5982"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Niu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100373698","display_name":"Xiao Dong Chen","orcid":"https://orcid.org/0000-0002-0150-0491"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052641450","display_name":"Zhijun Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijun Guo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100440920","display_name":"Dongsheng Li","orcid":"https://orcid.org/0000-0003-3103-8442"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongsheng Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Computer Science (National Pilot Software Engineering School),Beijing,China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5090467881"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":2.0598,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.8880557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1182","last_page":"1191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9988999962806702,"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":0.9988999962806702,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9979000091552734,"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/interpretability","display_name":"Interpretability","score":0.9230396747589111},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7839570045471191},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6299300193786621},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5920701622962952},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5599295496940613},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5549863576889038},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4513283967971802},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4382486641407013}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9230396747589111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839570045471191},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6299300193786621},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5920701622962952},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5599295496940613},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5549863576889038},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4513283967971802},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4382486641407013},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020471","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1983732626","https://openalex.org/W1989130706","https://openalex.org/W2002521620","https://openalex.org/W2013472039","https://openalex.org/W2016166233","https://openalex.org/W2041811496","https://openalex.org/W2059804518","https://openalex.org/W2064675550","https://openalex.org/W2069508080","https://openalex.org/W2117014758","https://openalex.org/W2132782512","https://openalex.org/W2209090757","https://openalex.org/W2754252319","https://openalex.org/W2897113013","https://openalex.org/W2901610790","https://openalex.org/W2905388713","https://openalex.org/W2962752580","https://openalex.org/W2963507686","https://openalex.org/W2963928450","https://openalex.org/W2964006226","https://openalex.org/W2971973336","https://openalex.org/W2980994438","https://openalex.org/W2998086450","https://openalex.org/W3005837895","https://openalex.org/W3022643593","https://openalex.org/W3115700835","https://openalex.org/W3118709533","https://openalex.org/W3131110071","https://openalex.org/W3132782787","https://openalex.org/W3168666980","https://openalex.org/W3170140111","https://openalex.org/W3171884590","https://openalex.org/W3208753490","https://openalex.org/W4250639944","https://openalex.org/W4307492541","https://openalex.org/W6736456187","https://openalex.org/W6742924651","https://openalex.org/W6754779804","https://openalex.org/W6763045174","https://openalex.org/W6764679822","https://openalex.org/W6770391498","https://openalex.org/W6786852218","https://openalex.org/W6797271739","https://openalex.org/W6804206276"],"related_works":["https://openalex.org/W3006943036","https://openalex.org/W3191046242","https://openalex.org/W4299487748","https://openalex.org/W4206493799","https://openalex.org/W4294031299","https://openalex.org/W2605281151","https://openalex.org/W3208423683","https://openalex.org/W4309045103","https://openalex.org/W4213225422","https://openalex.org/W3106591544"],"abstract_inverted_index":{"Time":[0],"series":[1,33,166],"forecasting":[2,37,72,85],"has":[3,55],"historically":[4],"been":[5,28],"a":[6,82,97],"popular":[7],"research":[8],"area,":[9],"attracting":[10],"widespread":[11],"interest":[12],"in":[13,49,161],"academia":[14],"and":[15,25,45,90,111,119,122,139],"industry.":[16],"Recently,":[17],"Deep":[18],"Learning":[19],"based":[20,87],"models":[21,38,54,138,141],"like":[22],"RNN,":[23],"LSTM,":[24],"NeuralProphet":[26,91,112],"have":[27],"successfully":[29],"applied":[30],"for":[31,164],"time":[32,165],"forecasting.":[34,167],"However,":[35],"single":[36,137],"are":[39],"unable":[40],"to":[41,57,62,106,115],"capture":[42],"full":[43],"information":[44],"learn":[46],"complex":[47],"patterns":[48],"data.":[50],"The":[51,101,149],"combination":[52],"of":[53,74,128,142],"proved":[56],"be":[58],"an":[59],"effective":[60],"strategy":[61],"address":[63],"this":[64,78,129],"issue.":[65],"Traditional":[66],"hybrid":[67,75,84,99,140,156],"structures":[68,144],"still":[69],"limit":[70],"the":[71,126],"ability":[73],"methods.":[76],"In":[77,125],"paper,":[79,130],"we":[80,131],"propose":[81],"novel":[83],"model":[86,103,107,134,157],"on":[88],"LSTM":[89,105],"(NP-LSTM),":[92],"which":[93],"is":[94,113],"constructed":[95],"by":[96],"parallel-series":[98],"structure.":[100],"proposed":[102],"uses":[104],"diverse":[108],"nonlinear":[109],"relationships":[110],"designed":[114],"extract":[116],"primary":[117],"trends":[118],"seasonal":[120],"effects":[121],"provide":[123],"interpretability.":[124],"experiments":[127],"compare":[132],"our":[133,154],"with":[135],"other":[136],"traditional":[143],"using":[145],"four":[146],"real-world":[147],"datasets.":[148],"experimental":[150],"results":[151],"show":[152],"that":[153],"NP-LSTM":[155],"obtains":[158],"superior":[159],"performance":[160],"various":[162],"metrics":[163]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}