{"id":"https://openalex.org/W4283693233","doi":"https://doi.org/10.1007/s11063-022-10944-0","title":"Attentional Gated Res2Net for Multivariate Time Series Classification","display_name":"Attentional Gated Res2Net for Multivariate Time Series Classification","publication_year":2022,"publication_date":"2022-06-29","ids":{"openalex":"https://openalex.org/W4283693233","doi":"https://doi.org/10.1007/s11063-022-10944-0"},"language":"en","primary_location":{"id":"doi:10.1007/s11063-022-10944-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-022-10944-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10944-0.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/s11063-022-10944-0.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072105113","display_name":"Chao Yang","orcid":"https://orcid.org/0000-0002-3763-5080"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Chao Yang","raw_affiliation_strings":["School of Computer Science, University of Technology Sydney, Sydney, NSW, 2007, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Technology Sydney, Sydney, NSW, 2007, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076107706","display_name":"Xianzhi Wang","orcid":"https://orcid.org/0000-0001-9582-3445"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xianzhi Wang","raw_affiliation_strings":["School of Computer Science, University of Technology Sydney, Sydney, NSW, 2007, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Technology Sydney, Sydney, NSW, 2007, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052731721","display_name":"Lina Yao","orcid":"https://orcid.org/0000-0002-4149-839X"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lina Yao","raw_affiliation_strings":["School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059227406","display_name":"Guodong Long","orcid":"https://orcid.org/0000-0003-3740-9515"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Guodong Long","raw_affiliation_strings":["Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia"],"affiliations":[{"raw_affiliation_string":"Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057139422","display_name":"Jing Jiang","orcid":"https://orcid.org/0000-0001-5301-7779"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jing Jiang","raw_affiliation_strings":["Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia"],"affiliations":[{"raw_affiliation_string":"Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051512158","display_name":"Guandong Xu","orcid":"https://orcid.org/0000-0003-4493-6663"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Guandong Xu","raw_affiliation_strings":["Data Science Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia"],"affiliations":[{"raw_affiliation_string":"Data Science Institute, University of Technology Sydney, Sydney, NSW, 2007, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5072105113"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.9357,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.85723816,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"55","issue":"2","first_page":"1371","last_page":"1395"},"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":0.9919000267982483,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9527000188827515,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.7829535007476807},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7034460306167603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5897384881973267},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48143312335014343},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4736517667770386},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.45559027791023254},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45064079761505127},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.44931429624557495},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4278605580329895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4133763313293457},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.41110625863075256}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7829535007476807},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7034460306167603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5897384881973267},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48143312335014343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4736517667770386},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45559027791023254},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45064079761505127},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.44931429624557495},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4278605580329895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4133763313293457},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.41110625863075256}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11063-022-10944-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-022-10944-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10944-0.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/s11063-022-10944-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-022-10944-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10944-0.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":[],"awards":[{"id":"https://openalex.org/G652412008","display_name":null,"funder_award_id":"LP180100654","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G8061068870","display_name":null,"funder_award_id":"DE180100251","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320320967","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283693233.pdf","grobid_xml":"https://content.openalex.org/works/W4283693233.grobid-xml"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W170732776","https://openalex.org/W275750519","https://openalex.org/W1526741802","https://openalex.org/W1536680647","https://openalex.org/W1975257359","https://openalex.org/W1981276685","https://openalex.org/W1988115241","https://openalex.org/W2020571458","https://openalex.org/W2045477744","https://openalex.org/W2061359421","https://openalex.org/W2064675550","https://openalex.org/W2081028405","https://openalex.org/W2082479672","https://openalex.org/W2097117768","https://openalex.org/W2118782025","https://openalex.org/W2144380653","https://openalex.org/W2157331557","https://openalex.org/W2163922914","https://openalex.org/W2166547175","https://openalex.org/W2168736308","https://openalex.org/W2193145675","https://openalex.org/W2531409750","https://openalex.org/W2549139847","https://openalex.org/W2555077524","https://openalex.org/W2581867724","https://openalex.org/W2583684860","https://openalex.org/W2585123518","https://openalex.org/W2598525681","https://openalex.org/W2612690371","https://openalex.org/W2752782242","https://openalex.org/W2783323081","https://openalex.org/W2884585870","https://openalex.org/W2888477829","https://openalex.org/W2904388869","https://openalex.org/W2905724129","https://openalex.org/W2919115771","https://openalex.org/W2928165649","https://openalex.org/W2963150697","https://openalex.org/W2963343478","https://openalex.org/W2963434542","https://openalex.org/W2971541730","https://openalex.org/W2972810968","https://openalex.org/W2973048981","https://openalex.org/W2981830988","https://openalex.org/W2981927672","https://openalex.org/W2982438846","https://openalex.org/W2988244882","https://openalex.org/W2996553713","https://openalex.org/W3022394231","https://openalex.org/W3036678194","https://openalex.org/W3042807565","https://openalex.org/W3080921724","https://openalex.org/W3083891030","https://openalex.org/W3090004808","https://openalex.org/W3106250896","https://openalex.org/W3174726724","https://openalex.org/W3177318507","https://openalex.org/W3200128628","https://openalex.org/W4220966602","https://openalex.org/W6604344240","https://openalex.org/W6605161895","https://openalex.org/W6633340474"],"related_works":["https://openalex.org/W4225852842","https://openalex.org/W4287776258","https://openalex.org/W3027997911","https://openalex.org/W2767651786","https://openalex.org/W2563096758","https://openalex.org/W3021430260","https://openalex.org/W2912288872","https://openalex.org/W2936488316","https://openalex.org/W4386053843","https://openalex.org/W3158004940"],"abstract_inverted_index":{"Abstract":[0],"Multivariate":[1,39],"time":[2,37,40,78],"series":[3,41,79],"classification":[4,33,174],"is":[5,179],"a":[6,45,66,161,170,201,219],"critical":[7],"problem":[8],"in":[9,53],"data":[10],"mining":[11],"with":[12],"broad":[13],"applications.":[14],"It":[15],"requires":[16],"harnessing":[17],"the":[18,31,36,57,101,105,108,137,167,173,183,193],"inter-relationship":[19],"of":[20,26,35,48,60,115,126,176,195],"multiple":[21,116],"variables":[22],"and":[23,50,92,131,157,208],"various":[24,54],"ranges":[25],"temporal":[27,61,95,139],"dependencies":[28],"to":[29,87,103,134,218],"assign":[30],"correct":[32],"label":[34],"series.":[38],"may":[42],"come":[43],"from":[44],"wide":[46],"range":[47],"sources":[49],"be":[51],"used":[52,199],"scenarios,":[55],"bringing":[56],"classifier":[58],"challenge":[59],"representation":[62],"learning.":[63],"We":[64],"propose":[65,123],"novel":[67],"convolutional":[68],"neural":[69],"network":[70],"architecture":[71],"called":[72],"Attentional":[73],"Gated":[74],"Res2Net":[75],"for":[76,118],"multivariate":[77,147],"classification.":[80],"Our":[81,141,164],"model":[82,102,153,165,178,191,217],"uses":[83],"hierarchical":[84],"residual-like":[85],"connections":[86],"achieve":[88],"multi-scale":[89],"receptive":[90,113],"fields":[91,114],"capture":[93],"multi-granular":[94,138],"information.":[96],"The":[97],"gating":[98],"mechanism":[99],"enables":[100],"consider":[104],"relations":[106],"between":[107],"feature":[109],"maps":[110],"extracted":[111],"by":[112,160,169],"sizes":[117],"information":[119],"fusion.":[120],"Further,":[121,203],"we":[122,187,210],"two":[124],"types":[125],"attention":[127,130],"modules,":[128],"channel-wise":[129],"block-wise":[132],"attention,":[133],"better":[135,181],"leverage":[136],"patterns.":[140],"experimental":[142],"results":[143],"on":[144,205,214],"14":[145],"benchmark":[146],"time-series":[148],"datasets":[149],"show":[150],"that":[151,189],"our":[152,177,190,206,216],"outperforms":[154,166],"several":[155],"baselines":[156],"state-of-the-art":[158],"methods":[159],"large":[162,171],"margin.":[163],"SOTA":[168,184],"margin,":[172],"accuracy":[175],"10.16%":[180],"than":[182],"model.":[185],"Besides,":[186],"demonstrate":[188],"improves":[192],"performance":[194],"existing":[196],"models":[197],"when":[198],"as":[200],"plugin.":[202],"based":[204],"experiments":[207],"analysis,":[209],"provide":[211],"practical":[212],"advice":[213],"applying":[215],"new":[220],"problem.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}