{"id":"https://openalex.org/W4319586741","doi":"https://doi.org/10.1109/dsaa54385.2022.10032335","title":"Multivariate Time Series Analysis: An Interpretable CNN-based Model","display_name":"Multivariate Time Series Analysis: An Interpretable CNN-based Model","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319586741","doi":"https://doi.org/10.1109/dsaa54385.2022.10032335"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa54385.2022.10032335","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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/A5079990847","display_name":"Raneen Younis","orcid":"https://orcid.org/0000-0002-0403-6495"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Raneen Younis","raw_affiliation_strings":["L3S Research Center,Hannover,Germany","L3S Research Center, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S Research Center,Hannover,Germany","institution_ids":["https://openalex.org/I4210136150"]},{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076017695","display_name":"Sergej Zerr","orcid":"https://orcid.org/0000-0001-7587-8385"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sergej Zerr","raw_affiliation_strings":["L3S Research Center,Hannover,Germany","L3S Research Center, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S Research Center,Hannover,Germany","institution_ids":["https://openalex.org/I4210136150"]},{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035434415","display_name":"Zahra Ahmadi","orcid":"https://orcid.org/0000-0003-1110-4756"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Zahra Ahmadi","raw_affiliation_strings":["L3S Research Center,Hannover,Germany","L3S Research Center, Hannover, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S Research Center,Hannover,Germany","institution_ids":["https://openalex.org/I4210136150"]},{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3314,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.83600881,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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/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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9063090085983276},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.76039719581604},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7328810691833496},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6477733850479126},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.610651433467865},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6074103116989136},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5890403389930725},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5614414215087891},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5286795496940613},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4924374520778656},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4724041819572449},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.47184303402900696},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4349074959754944},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4178086519241333}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9063090085983276},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.76039719581604},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7328810691833496},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6477733850479126},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.610651433467865},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6074103116989136},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5890403389930725},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5614414215087891},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5286795496940613},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4924374520778656},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4724041819572449},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.47184303402900696},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4349074959754944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4178086519241333},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa54385.2022.10032335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa54385.2022.10032335","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W16794263","https://openalex.org/W1787224781","https://openalex.org/W1849277567","https://openalex.org/W1850325679","https://openalex.org/W1903029394","https://openalex.org/W1997102766","https://openalex.org/W2037537012","https://openalex.org/W2098759488","https://openalex.org/W2130942839","https://openalex.org/W2148443418","https://openalex.org/W2194775991","https://openalex.org/W2195388612","https://openalex.org/W2295107390","https://openalex.org/W2306394264","https://openalex.org/W2516809705","https://openalex.org/W2551393996","https://openalex.org/W2571694389","https://openalex.org/W2598525681","https://openalex.org/W2808955427","https://openalex.org/W2892035503","https://openalex.org/W2962858109","https://openalex.org/W2963233086","https://openalex.org/W2963434542","https://openalex.org/W2988244882","https://openalex.org/W3004401633","https://openalex.org/W3011806746","https://openalex.org/W3017451302","https://openalex.org/W3086598148","https://openalex.org/W3106591544","https://openalex.org/W3135149107","https://openalex.org/W3136666665","https://openalex.org/W3137380298","https://openalex.org/W3171152739","https://openalex.org/W4246193833","https://openalex.org/W4289360400","https://openalex.org/W4297971002","https://openalex.org/W6679436768","https://openalex.org/W6698169118","https://openalex.org/W6736518430","https://openalex.org/W6755529311","https://openalex.org/W6776368443","https://openalex.org/W6784822222"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W4311431240","https://openalex.org/W4312407344","https://openalex.org/W4384115502","https://openalex.org/W4226258012","https://openalex.org/W4383681494"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks,":[2],"especially":[3],"the":[4,21,44,56,67,74,87,99,107,124,130,137,144,147,158,162,165,169,174,180,192],"Convolutional":[5],"Neural":[6],"Network":[7],"(CNN)":[8],"models,":[9,49],"have":[10],"shown":[11],"promising":[12],"results":[13,186],"in":[14,123,143,187,191],"multivariate":[15,125,181],"time":[16,52,76,101],"series":[17,53,77,102],"data":[18,54,131],"analysis.":[19],"However,":[20],"predictions":[22,45,149],"of":[23,121,146,153,164,179],"these":[24,48],"data-driven":[25],"black-box":[26],"models":[27],"are":[28,177],"tough":[29],"to":[30,39,65,110,157],"interpret":[31,66],"from":[32,80,98],"a":[33,62,81,188],"human":[34],"perspective,":[35],"making":[36],"it":[37],"questionable":[38],"trust":[40],"and":[41,72,94,115,128,150,161,183],"rely":[42],"on":[43,136],"made":[46],"by":[47,70],"specifically":[50],"for":[51,90],"with":[55],"append-only":[57],"feature.":[58],"This":[59],"paper":[60],"proposes":[61],"new":[63],"approach":[64,105],"CNN":[68],"outputs":[69],"extracting":[71],"clustering":[73],"activated":[75],"sequences":[78,85],"learned":[79],"trained":[82],"network.":[83],"These":[84],"show":[86],"representative":[88,132,178],"features":[89],"each":[91,112,154],"output":[92,159],"label":[93,160],"form":[95],"interpretable":[96],"representations":[97],"original":[100],"data.":[103],"Our":[104,134],"is":[106],"first":[108],"framework":[109],"identify":[111],"signal\u2019s":[113],"role":[114],"dependencies,":[116],"consider":[117],"all":[118],"possible":[119],"combinations":[120],"signals":[122],"time-series":[126],"input,":[127],"visualize":[129],"features.":[133],"experiments":[135,171],"Baydogan\u2019s":[138],"archive":[139],"indicate":[140],"remarkable":[141],"improvements":[142],"interpretability":[145],"network":[148,166],"relation":[151],"identification":[152],"input":[155,182],"signal":[156],"channels":[163],"layers.":[167],"Furthermore,":[168],"conducted":[170],"confirm":[172],"that":[173],"extracted":[175],"patterns":[176],"changing":[184],"them":[185],"drastic":[189],"reduction":[190],"prediction":[193],"accuracy.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
