{"id":"https://openalex.org/W2937294679","doi":"https://doi.org/10.1109/icassp.2019.8682410","title":"Dual-stream CNN for Structured Time Series Classification","display_name":"Dual-stream CNN for Structured Time Series Classification","publication_year":2019,"publication_date":"2019-04-16","ids":{"openalex":"https://openalex.org/W2937294679","doi":"https://doi.org/10.1109/icassp.2019.8682410","mag":"2937294679"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5033873637","display_name":"Shuchen Weng","orcid":"https://orcid.org/0000-0003-0777-5055"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuchen Weng","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336576","display_name":"Wenbo Li","orcid":"https://orcid.org/0000-0002-3122-5400"},"institutions":[{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Wenbo Li","raw_affiliation_strings":["SUNY Computer Science Department, University at Albany"],"affiliations":[{"raw_affiliation_string":"SUNY Computer Science Department, University at Albany","institution_ids":["https://openalex.org/I392282","https://openalex.org/I4210156583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100653792","display_name":"Yi Zhang","orcid":"https://orcid.org/0000-0003-2297-2954"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Zhang","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023752172","display_name":"Siwei Lyu","orcid":"https://orcid.org/0000-0002-0992-685X"},"institutions":[{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]},{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Siwei Lyu","raw_affiliation_strings":["SUNY Computer Science Department, University at Albany"],"affiliations":[{"raw_affiliation_string":"SUNY Computer Science Department, University at Albany","institution_ids":["https://openalex.org/I392282","https://openalex.org/I4210156583"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033873637"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.6634,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.67257886,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3187","last_page":"3191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"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":1.0,"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":0.9987000226974487,"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":0.9729999899864197,"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/computer-science","display_name":"Computer science","score":0.8505746126174927},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.7426700592041016},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.7126307487487793},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.698393702507019},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.6587940454483032},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6562280654907227},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6370062828063965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.602541983127594},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5411216020584106},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.46794596314430237},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4600619375705719},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41286277770996094},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.343997597694397},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07559379935264587}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8505746126174927},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.7426700592041016},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.7126307487487793},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.698393702507019},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.6587940454483032},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6562280654907227},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6370062828063965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.602541983127594},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5411216020584106},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.46794596314430237},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4600619375705719},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41286277770996094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.343997597694397},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07559379935264587},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":44,"referenced_works":["https://openalex.org/W846669277","https://openalex.org/W1522301498","https://openalex.org/W1596216457","https://openalex.org/W1950788856","https://openalex.org/W1983592444","https://openalex.org/W1985469025","https://openalex.org/W2048821851","https://openalex.org/W2056339039","https://openalex.org/W2097117768","https://openalex.org/W2100774779","https://openalex.org/W2108036388","https://openalex.org/W2113152322","https://openalex.org/W2143267104","https://openalex.org/W2144380653","https://openalex.org/W2144499799","https://openalex.org/W2156303437","https://openalex.org/W2162550962","https://openalex.org/W2168328261","https://openalex.org/W2230000137","https://openalex.org/W2442651457","https://openalex.org/W2510185399","https://openalex.org/W2556782416","https://openalex.org/W2567070169","https://openalex.org/W2604321021","https://openalex.org/W2606294640","https://openalex.org/W2613904329","https://openalex.org/W2736334449","https://openalex.org/W2736995747","https://openalex.org/W2776687321","https://openalex.org/W2778523960","https://openalex.org/W2950568498","https://openalex.org/W2963970792","https://openalex.org/W2964121744","https://openalex.org/W2964265128","https://openalex.org/W3098538019","https://openalex.org/W6631190155","https://openalex.org/W6635654010","https://openalex.org/W6640754710","https://openalex.org/W6682864246","https://openalex.org/W6725062358","https://openalex.org/W6730028046","https://openalex.org/W6731370813","https://openalex.org/W6737778391","https://openalex.org/W6741186868"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W2250140200","https://openalex.org/W2016108640"],"abstract_inverted_index":{"The":[0,120],"structured":[1],"time":[2],"series":[3],"(STS)":[4],"classification":[5],"problem":[6],"requires":[7],"the":[8,24,27,60,74],"modeling":[9,73],"of":[10,26,98,122],"interweaved":[11,75],"spatiotemporal":[12,76],"dependency.":[13],"Most":[14],"previous":[15],"methods":[16],"model":[17,105,124],"these":[18],"two":[19],"dependencies":[20],"independently.":[21],"Due":[22],"to":[23,49,89,114,117],"complexity":[25],"STS":[28,96],"data,":[29],"we":[30,66],"argue":[31],"that":[32,41,87],"a":[33,38,51,68,80],"desirable":[34],"method":[35],"should":[36],"be":[37,115],"holistic":[39],"framework":[40,71,86],"is":[42,106,125],"adaptive":[43],"and":[44,78,93,101,109],"flexible.":[45],"This":[46],"motivates":[47],"us":[48],"design":[50],"deep":[52],"neural":[53,64,82],"network":[54,83],"with":[55],"such":[56],"merits.":[57],"Inspired":[58],"by":[59],"dual-stream":[61,70],"hypothesis":[62],"in":[63,95],"science,":[65],"propose":[67],"novel":[69],"for":[72,132],"dependency,":[77],"develop":[79],"convolutional":[81],"within":[84],"this":[85],"aims":[88],"achieve":[90],"high":[91],"adaptability":[92],"flexibility":[94],"configurations":[97],"sequential":[99],"order":[100],"dependency":[102],"range.":[103],"Our":[104],"highly":[107],"modularized":[108],"scalable,":[110],"making":[111],"it":[112],"easy":[113],"adapted":[116],"specific":[118],"tasks.":[119],"effectiveness":[121],"our":[123],"demonstrated":[126],"through":[127],"experiments":[128],"on":[129],"benchmark":[130],"datasets":[131],"skeleton":[133],"based":[134],"activity":[135],"recognition.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
