{"id":"https://openalex.org/W3010878697","doi":"https://doi.org/10.1109/access.2020.2981506","title":"A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting","display_name":"A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3010878697","doi":"https://doi.org/10.1109/access.2020.2981506","mag":"3010878697"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2981506","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2981506","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039658.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039658.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005463262","display_name":"Fagui Liu","orcid":"https://orcid.org/0000-0003-1135-4982"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fagui Liu","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1135-4982","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006218609","display_name":"Yunsheng Lu","orcid":"https://orcid.org/0000-0001-9237-6707"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunsheng Lu","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9237-6707","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001051752","display_name":"Misheng Cai","orcid":"https://orcid.org/0000-0002-8863-2576"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muqing Cai","raw_affiliation_strings":["School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8863-2576","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005463262"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.9502,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.94461498,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"62423","last_page":"62438"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9980000257492065,"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":0.9957000017166138,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8033754229545593},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7104244828224182},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7066154479980469},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6372627019882202},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5918439626693726},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5843939185142517},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.506510853767395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46393588185310364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4263209104537964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3350386619567871},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3231067955493927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8033754229545593},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7104244828224182},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7066154479980469},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6372627019882202},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5918439626693726},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5843939185142517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.506510853767395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46393588185310364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4263209104537964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3350386619567871},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3231067955493927},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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":2,"locations":[{"id":"doi:10.1109/access.2020.2981506","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2981506","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039658.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:23ee92b4bc9347678475047c0da7a775","is_oa":true,"landing_page_url":"https://doaj.org/article/23ee92b4bc9347678475047c0da7a775","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 62423-62438 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2981506","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2981506","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039658.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3010878697.pdf","grobid_xml":"https://content.openalex.org/works/W3010878697.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1862394037","https://openalex.org/W1964058989","https://openalex.org/W1971665758","https://openalex.org/W1972324604","https://openalex.org/W2030034065","https://openalex.org/W2033885641","https://openalex.org/W2038751902","https://openalex.org/W2051403129","https://openalex.org/W2064675550","https://openalex.org/W2075795319","https://openalex.org/W2076063813","https://openalex.org/W2077447589","https://openalex.org/W2113244375","https://openalex.org/W2120390927","https://openalex.org/W2126709108","https://openalex.org/W2126831543","https://openalex.org/W2129270932","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2155482907","https://openalex.org/W2157331557","https://openalex.org/W2160507653","https://openalex.org/W2172064003","https://openalex.org/W2194775991","https://openalex.org/W2560675361","https://openalex.org/W2604847698","https://openalex.org/W2613328025","https://openalex.org/W2734777338","https://openalex.org/W2754252319","https://openalex.org/W2760894977","https://openalex.org/W2786619647","https://openalex.org/W2794103404","https://openalex.org/W2804687177","https://openalex.org/W2807694716","https://openalex.org/W2807925766","https://openalex.org/W2808535700","https://openalex.org/W2841644753","https://openalex.org/W2889480665","https://openalex.org/W2891511127","https://openalex.org/W2894821558","https://openalex.org/W2905238323","https://openalex.org/W2917733937","https://openalex.org/W2919115771","https://openalex.org/W2943225778","https://openalex.org/W2948490758","https://openalex.org/W2963558246","https://openalex.org/W2964010366","https://openalex.org/W2964308564","https://openalex.org/W2970546901","https://openalex.org/W2971689447","https://openalex.org/W3150435615","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6687483927","https://openalex.org/W6730235577","https://openalex.org/W6747916571","https://openalex.org/W6752537898","https://openalex.org/W6754695959"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W2788972299","https://openalex.org/W1861848143","https://openalex.org/W2118640767","https://openalex.org/W2622688551","https://openalex.org/W2119012848","https://openalex.org/W1990205660","https://openalex.org/W1550175370","https://openalex.org/W4387331850"],"abstract_inverted_index":{"Multivariate":[0],"Time":[1],"Series":[2],"Forecasting":[3],"(MTSF)":[4],"has":[5,179],"recently":[6],"emerged":[7],"its":[8],"growing":[9],"importance":[10],"in":[11,25,38],"many":[12],"industries.":[13],"However,":[14],"how":[15],"to":[16,103,182,204],"reduce":[17,104],"the":[18,21,77,80,100,105,108,114,117,135,143,149,155,173,177,185,189,192,195,206,210,217,230],"influence":[19],"of":[20,69,111,172,176,188,198,209,221],"noise":[22],"components":[23],"existing":[24],"time":[26,109,160,212],"series":[27,82,121],"on":[28,43,54,134,225],"prediction":[29,51,115,186,208],"and":[30,47,74,97,107,119,154,219,234],"extract":[31],"features":[32,157],"effectively":[33,147],"are":[34],"still":[35],"two":[36,45],"challenges":[37,46],"MTSF.":[39],"This":[40],"paper":[41],"focuses":[42],"those":[44],"proposes":[48],"a":[49,163],"new":[50],"method":[52,67,224],"based":[53,133],"decomposition-ensemble":[55],"framework":[56],"called":[57],"adaptive":[58],"sub-series":[59,118,128,200],"clustering-stacked":[60],"residual":[61,140],"LSTMs-multi-level":[62],"attention":[63,165],"mechanism":[64,166],"(ASC-SRLSTMs-MLAttn).":[65],"The":[66,130],"consists":[68],"three":[70],"stages:":[71],"decomposition,":[72],"prediction,":[73],"ensemble.":[75],"In":[76,113,191],"decomposition":[78],"stage,":[79,116,194],"target":[81,211],"is":[83,132],"decomposed":[84],"by":[85,99],"Ensemble":[86],"Empirical":[87],"Mode":[88],"Decomposition":[89],"into":[90,125],"multiple":[91],"sub-series,":[92],"which":[93,145,168],"will":[94,122,201],"be":[95,123,202],"clustered":[96],"reconstructed":[98],"ASC":[101],"algorithm":[102],"complexity":[106],"consumption":[110],"prediction.":[112,129],"correlation":[120],"fed":[124],"SRLSTMs-MLAttn":[126],"for":[127],"model":[131],"encoder-decoder":[136],"architecture":[137],"with":[138],"stacked":[139],"LSTMs":[141],"as":[142],"encoder,":[144,178],"can":[146],"capture":[148],"dependencies":[150],"among":[151],"multi":[152],"variables":[153],"temporal":[156],"from":[158],"multivariate":[159],"series.":[161,213],"Besides,":[162],"multi-level":[164],"(MLAttn),":[167],"makes":[169],"full":[170],"use":[171],"encoding":[174],"information":[175],"been":[180],"introduced":[181],"further":[183],"improve":[184],"performance":[187],"model.":[190],"ensemble":[193],"predicted":[196],"values":[197],"each":[199],"summed":[203],"obtain":[205],"final":[207],"We":[214],"also":[215],"demonstrate":[216],"superiority":[218],"effectiveness":[220],"our":[222],"proposed":[223],"four":[226],"public":[227],"datasets":[228],"via":[229],"conducted":[231],"comparison":[232],"experiment":[233],"ablation":[235],"study.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}