{"id":"https://openalex.org/W4391610052","doi":"https://doi.org/10.1007/s11063-024-11531-1","title":"TSCF: An Improved Deep Forest Model for Time Series Classification","display_name":"TSCF: An Improved Deep Forest Model for Time Series Classification","publication_year":2024,"publication_date":"2024-02-07","ids":{"openalex":"https://openalex.org/W4391610052","doi":"https://doi.org/10.1007/s11063-024-11531-1"},"language":"en","primary_location":{"id":"doi:10.1007/s11063-024-11531-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-024-11531-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11531-1.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11531-1.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106749518","display_name":"Mingxin Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingxin Dai","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","institution_ids":["https://openalex.org/I4210127216"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050517295","display_name":"Jidong Yuan","orcid":"https://orcid.org/0000-0003-2654-3372"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jidong Yuan","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","institution_ids":["https://openalex.org/I4210127216"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087708777","display_name":"Haiyang Liu","orcid":"https://orcid.org/0000-0003-0397-5770"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyang Liu","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","institution_ids":["https://openalex.org/I4210127216"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100344683","display_name":"Jinfeng Wang","orcid":"https://orcid.org/0000-0002-6687-9420"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210127216","display_name":"Ministry of Transport","ror":"https://ror.org/031wq1t38","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210127216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinfeng Wang","raw_affiliation_strings":["Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China","institution_ids":["https://openalex.org/I4210127216"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5106749518"],"corresponding_institution_ids":["https://openalex.org/I21193070","https://openalex.org/I4210127216"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":2.5076,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89486254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"56","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9696999788284302,"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"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9656999707221985,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.887911319732666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6926533579826355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6511250138282776},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5862006545066833},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5668080449104309},{"id":"https://openalex.org/keywords/subsequence","display_name":"Subsequence","score":0.5496441721916199},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5125524997711182},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4959631860256195},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46970316767692566},{"id":"https://openalex.org/keywords/computational-intelligence","display_name":"Computational intelligence","score":0.464762806892395},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4459286630153656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4371717572212219},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3920513093471527},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1715323030948639}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.887911319732666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6926533579826355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6511250138282776},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5862006545066833},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5668080449104309},{"id":"https://openalex.org/C137877099","wikidata":"https://www.wikidata.org/wiki/Q1332977","display_name":"Subsequence","level":3,"score":0.5496441721916199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5125524997711182},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4959631860256195},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46970316767692566},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.464762806892395},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4459286630153656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4371717572212219},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3920513093471527},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1715323030948639},{"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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11063-024-11531-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-024-11531-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11531-1.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11063-024-11531-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-024-11531-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11531-1.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.7200000286102295,"display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G2991921791","display_name":null,"funder_award_id":"No. 2022JBMC011","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4141938963","display_name":null,"funder_award_id":"2022JBMC011","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6898813437","display_name":null,"funder_award_id":"No. 2022YFE0200400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391610052.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W789250018","https://openalex.org/W1968354112","https://openalex.org/W1975257359","https://openalex.org/W1978371851","https://openalex.org/W2008348094","https://openalex.org/W2029438113","https://openalex.org/W2050493487","https://openalex.org/W2118529802","https://openalex.org/W2123502857","https://openalex.org/W2166547175","https://openalex.org/W2194775991","https://openalex.org/W2410961113","https://openalex.org/W2468738844","https://openalex.org/W2477490855","https://openalex.org/W2524083015","https://openalex.org/W2551393996","https://openalex.org/W2581867724","https://openalex.org/W2585354796","https://openalex.org/W2598525681","https://openalex.org/W2767826044","https://openalex.org/W2786161686","https://openalex.org/W2888791883","https://openalex.org/W2892035503","https://openalex.org/W2954112873","https://openalex.org/W2967988901","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W2985027632","https://openalex.org/W3010158807","https://openalex.org/W3042807565","https://openalex.org/W3080921724","https://openalex.org/W3083891030","https://openalex.org/W3098967488","https://openalex.org/W3128007949","https://openalex.org/W3186145246","https://openalex.org/W4206036274","https://openalex.org/W4232714830","https://openalex.org/W6633340474"],"related_works":["https://openalex.org/W2996768723","https://openalex.org/W4212895949","https://openalex.org/W4230102134","https://openalex.org/W4247866870","https://openalex.org/W2112772040","https://openalex.org/W3216774880","https://openalex.org/W2078387789","https://openalex.org/W2051502035","https://openalex.org/W4253793592","https://openalex.org/W2889302474"],"abstract_inverted_index":{"Abstract":[0],"The":[1,109],"deep":[2,15,27,40,121,132],"forest":[3,28,41,59],"presents":[4],"a":[5,61],"novel":[6],"approach":[7],"that":[8,123,137],"yields":[9],"competitive":[10],"performance":[11],"when":[12],"compared":[13],"to":[14,29,116],"neural":[16],"networks.":[17],"Nevertheless,":[18],"there":[19],"are":[20],"limited":[21],"studies":[22],"on":[23,70,93,107],"the":[24,36,45,94,126,130],"application":[25],"of":[26,39,48,112],"time":[30,49,56],"series":[31,57],"classification":[32],"(TSC)":[33],"tasks,":[34],"and":[35,83,102,120,129],"direct":[37],"use":[38],"cannot":[42],"effectively":[43],"capture":[44],"relevant":[46],"characteristics":[47],"series.":[50],"For":[51],"that,":[52],"this":[53,113],"paper":[54],"proposes":[55],"cascade":[58],"(TSCF),":[60],"model":[62],"specifically":[63],"designed":[64],"for":[65,90,143],"TSC":[66,145],"tasks.":[67],"TSCF":[68,138],"relies":[69],"four":[71],"base":[72],"classifiers,":[73],"i.e.,":[74],"random":[75,78,80],"forest,":[76,79,82,88],"completely":[77],"shapelet":[81],"diverse":[84],"representation":[85],"canonical":[86],"interval":[87],"allowing":[89],"feature":[91],"learning":[92],"original":[95,131],"data":[96],"from":[97],"three":[98],"granularities:":[99],"point,":[100],"subsequence,":[101],"summary":[103],"statistics":[104],"calculated":[105],"based":[106],"intervals.":[108],"major":[110],"contribution":[111],"work,":[114],"is":[115],"define":[117],"an":[118],"ensemble":[119],"classifier":[122],"significantly":[124],"outperforms":[125,139],"individual":[127],"classifiers":[128],"forest.":[133],"Experimental":[134],"results":[135],"show":[136],"other":[140],"forest-based":[141],"algorithms":[142],"solving":[144],"problems.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
