{"id":"https://openalex.org/W2785597854","doi":"https://doi.org/10.1109/pimrc.2017.8292668","title":"Research on machine learning algorithms and feature extraction for time series","display_name":"Research on machine learning algorithms and feature extraction for time series","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2785597854","doi":"https://doi.org/10.1109/pimrc.2017.8292668","mag":"2785597854"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2017.8292668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2017.8292668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","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/A5100440301","display_name":"Lei Li","orcid":"https://orcid.org/0000-0002-3204-6527"},"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":"Lei Li","raw_affiliation_strings":["School of Computer, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113832112","display_name":"Yabin Wu","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":"Yabin Wu","raw_affiliation_strings":["School of Computer, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045397913","display_name":"Yihang Ou","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":"Yihang Ou","raw_affiliation_strings":["School of Computer, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350178","display_name":"Qi Li","orcid":"https://orcid.org/0000-0002-0033-6204"},"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":"Qi Li","raw_affiliation_strings":["School of Computer, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110579844","display_name":"Yanquan Zhou","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":"Yanquan Zhou","raw_affiliation_strings":["School of Computer, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016299438","display_name":"Daoxin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daoxin Chen","raw_affiliation_strings":["CapInfo Company Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"CapInfo Company Limited, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100440301"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":3.065,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.90931738,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"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":0.9957000017166138,"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/support-vector-machine","display_name":"Support vector machine","score":0.8506286144256592},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7969831228256226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.747390627861023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.719588041305542},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5662869811058044},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5510648488998413},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5365388989448547},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.49683240056037903},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47610601782798767},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4250982403755188},{"id":"https://openalex.org/keywords/ranking-svm","display_name":"Ranking SVM","score":0.4103184938430786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3908231854438782},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3441349267959595}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8506286144256592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7969831228256226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.747390627861023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.719588041305542},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5662869811058044},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5510648488998413},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5365388989448547},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.49683240056037903},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47610601782798767},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4250982403755188},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.4103184938430786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3908231854438782},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3441349267959595},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/pimrc.2017.8292668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2017.8292668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2012079387","https://openalex.org/W2168156818","https://openalex.org/W2533328922","https://openalex.org/W2572939427","https://openalex.org/W2609874355","https://openalex.org/W2766040222","https://openalex.org/W2790718085"],"related_works":["https://openalex.org/W3013515612","https://openalex.org/W2136184105","https://openalex.org/W2187500075","https://openalex.org/W2336974148","https://openalex.org/W2345184372","https://openalex.org/W2546942002","https://openalex.org/W4316658362","https://openalex.org/W2146204105","https://openalex.org/W2111376815","https://openalex.org/W4291214623"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,56],"use":[4],"various":[5],"machine":[6,47],"learning":[7],"algorithms":[8,15,55,83],"and":[9,16,39,53,69,74,120],"explore":[10],"the":[11,19,27,31,58,77,89,94,97,102,115,122],"influence":[12],"between":[13],"different":[14],"multi-feature":[17,67],"in":[18,86,133],"time":[20,28],"series.":[21],"The":[22],"real":[23],"consumption":[24,36,60],"records":[25],"constitute":[26],"series":[29],"as":[30],"research":[32],"object.":[33],"We":[34],"extract":[35],"mark,":[37],"frequency":[38],"other":[40,54,95],"features.":[41],"Moreover,":[42],"we":[43,63],"utilize":[44],"support":[45],"vector":[46],"(SVM),":[48],"long":[49],"short-term":[50],"memory":[51],"(LSTM)":[52],"predict":[57],"user's":[59],"behavior.":[61],"Besides,":[62],"have":[64],"also":[65],"implemented":[66],"fusion":[68,71,110],"multi-algorithm":[70],"with":[72],"LSTM":[73,82,119],"SVM.":[75,125],"Eventually,":[76],"experimental":[78],"results":[79],"show":[80],"that":[81],"is":[84,91,99,104,130],"advantageous":[85],"prediction":[87],"when":[88,101],"data":[90,103],"sparse.":[92],"In":[93,126],"hand,":[96],"SVM":[98],"beneficial":[100],"more":[105],"abundant.":[106],"What's":[107],"more,":[108],"LSTM-SVM":[109,129],"model":[111],"has":[112],"advantages":[113],"on":[114,121],"extracting":[116],"features":[117],"of":[118,124],"classification":[123],"most":[127,131],"cases,":[128],"outstanding":[132],"prediction.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
