{"id":"https://openalex.org/W2551393996","doi":"https://doi.org/10.1109/ijcnn.2017.7966039","title":"Time series classification from scratch with deep neural networks: A strong baseline","display_name":"Time series classification from scratch with deep neural networks: A strong baseline","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2551393996","doi":"https://doi.org/10.1109/ijcnn.2017.7966039","mag":"2551393996"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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/A5100637055","display_name":"Zhiguang Wang","orcid":"https://orcid.org/0000-0001-7045-5308"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhiguang Wang","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073342710","display_name":"Weizhong Yan","orcid":"https://orcid.org/0000-0002-7916-8476"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weizhong Yan","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114778025","display_name":"Tim Oates","orcid":"https://orcid.org/0000-0002-8655-747X"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tim Oates","raw_affiliation_strings":["Computer Science and Electric Engineering, University of Maryland"],"affiliations":[{"raw_affiliation_string":"Computer Science and Electric Engineering, University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100637055"],"corresponding_institution_ids":["https://openalex.org/I4210134512"],"apc_list":null,"apc_paid":null,"fwci":79.8415,"has_fulltext":false,"cited_by_count":1973,"citation_normalized_percentile":{"value":0.99979567,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1578","last_page":"1585"},"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":0.97079998254776,"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":0.9692000150680542,"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/computer-science","display_name":"Computer science","score":0.7929444313049316},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.7756575345993042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.684502124786377},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6711698770523071},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6481912732124329},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.5923149585723877},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5662965774536133},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.540514349937439},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5318652391433716},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49338117241859436},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.48991674184799194},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4597603976726532},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44989651441574097},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4467697739601135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4392208755016327},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4152521789073944},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.41294485330581665},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.412793904542923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7929444313049316},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.7756575345993042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.684502124786377},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6711698770523071},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6481912732124329},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.5923149585723877},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5662965774536133},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.540514349937439},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5318652391433716},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49338117241859436},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.48991674184799194},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4597603976726532},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44989651441574097},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4467697739601135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4392208755016327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4152521789073944},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.41294485330581665},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.412793904542923},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1836465849","https://openalex.org/W1850325679","https://openalex.org/W1903029394","https://openalex.org/W1921523184","https://openalex.org/W1968354112","https://openalex.org/W1975257359","https://openalex.org/W2035104901","https://openalex.org/W2050493487","https://openalex.org/W2091921805","https://openalex.org/W2095705004","https://openalex.org/W2101713460","https://openalex.org/W2164274563","https://openalex.org/W2194775991","https://openalex.org/W2237307454","https://openalex.org/W2295107390","https://openalex.org/W2306394264","https://openalex.org/W2475334473","https://openalex.org/W6637242042","https://openalex.org/W6674330103","https://openalex.org/W6689847659"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W2389417819"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,98,107],"simple":[3,99],"but":[4],"strong":[5],"baseline":[6,19],"for":[7,91,101,111],"time":[8],"series":[9],"classification":[10,134],"from":[11],"scratch":[12],"with":[13,57],"deep":[14,54],"neural":[15,55],"networks.":[16],"Our":[17,95],"proposed":[18,36],"models":[20,96],"are":[21],"pure":[22],"end-to-end":[23],"without":[24],"any":[25],"heavy":[26],"preprocessing":[27],"on":[28],"the":[29,52,58,73,76,84,88,92,102,112,122,133],"raw":[30,89],"data":[31,90],"or":[32],"feature":[33],"crafting.":[34],"The":[35,64],"Fully":[37],"Convolutional":[38],"Network":[39],"(FCN)":[40],"achieves":[41],"premium":[42],"performance":[43],"to":[44,81,120],"other":[45],"state-of-the-art":[46],"approaches":[47],"and":[48,106,132],"our":[49,69,126],"exploration":[50],"of":[51,75,125],"very":[53],"networks":[56],"ResNet":[59],"structure":[60],"is":[61,118],"also":[62],"competitive.":[63],"global":[65],"average":[66],"pooling":[67],"in":[68,87],"convolutional":[70],"model":[71],"enables":[72],"exploitation":[74],"Class":[77],"Activation":[78],"Map":[79],"(CAM)":[80],"find":[82],"out":[83],"contributing":[85],"region":[86],"specific":[93],"labels.":[94],"provides":[97],"choice":[100],"real":[103],"world":[104],"application":[105],"good":[108],"starting":[109],"point":[110],"future":[113],"research.":[114],"An":[115],"overall":[116],"analysis":[117],"provided":[119],"discuss":[121],"generalization":[123],"capability":[124],"models,":[127],"learned":[128],"features,":[129],"network":[130],"structures":[131],"semantics.":[135]},"counts_by_year":[{"year":2026,"cited_by_count":49},{"year":2025,"cited_by_count":301},{"year":2024,"cited_by_count":305},{"year":2023,"cited_by_count":308},{"year":2022,"cited_by_count":287},{"year":2021,"cited_by_count":294},{"year":2020,"cited_by_count":237},{"year":2019,"cited_by_count":134},{"year":2018,"cited_by_count":49},{"year":2017,"cited_by_count":8}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}