{"id":"https://openalex.org/W4211173242","doi":"https://doi.org/10.1109/icnsc52481.2021.9702161","title":"Time Series Anomaly Detection Based on CEEMDAN and LSTM","display_name":"Time Series Anomaly Detection Based on CEEMDAN and LSTM","publication_year":2021,"publication_date":"2021-12-03","ids":{"openalex":"https://openalex.org/W4211173242","doi":"https://doi.org/10.1109/icnsc52481.2021.9702161"},"language":"en","primary_location":{"id":"doi:10.1109/icnsc52481.2021.9702161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc52481.2021.9702161","pdf_url":null,"source":{"id":"https://openalex.org/S4363608459","display_name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","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":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5084548561","display_name":"Y.G. Anil Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yucong Rao","raw_affiliation_strings":["Nanjing University,Department of Control and Systems Engineering,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,Department of Control and Systems Engineering,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041797259","display_name":"Jiabao Zhao","orcid":"https://orcid.org/0000-0002-2186-3177"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiabao Zhao","raw_affiliation_strings":["Nanjing University,Department of Control and Systems Engineering,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,Department of Control and Systems Engineering,Nanjing,China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9973999857902527,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.7453011870384216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6839723587036133},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6783369183540344},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6524569988250732},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6313816905021667},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6016079187393188},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.5629875659942627},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5523335933685303},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.522984504699707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5190240144729614},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.4795213043689728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4653065800666809},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44934970140457153},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4285348951816559},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34024709463119507},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16718891263008118},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.14886459708213806},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07215860486030579}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7453011870384216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6839723587036133},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6783369183540344},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6524569988250732},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6313816905021667},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6016079187393188},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.5629875659942627},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5523335933685303},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.522984504699707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5190240144729614},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.4795213043689728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4653065800666809},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44934970140457153},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4285348951816559},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34024709463119507},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16718891263008118},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.14886459708213806},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07215860486030579},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnsc52481.2021.9702161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc52481.2021.9702161","pdf_url":null,"source":{"id":"https://openalex.org/S4363608459","display_name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","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":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.41999998688697815,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1975970972","https://openalex.org/W2007221293","https://openalex.org/W2024419291","https://openalex.org/W2046794274","https://openalex.org/W2049058890","https://openalex.org/W2093606067","https://openalex.org/W2120390927","https://openalex.org/W2122646361","https://openalex.org/W2125056386","https://openalex.org/W2167152259","https://openalex.org/W2186910770","https://openalex.org/W2584408238","https://openalex.org/W2586634262","https://openalex.org/W2786088545","https://openalex.org/W2789534793","https://openalex.org/W2794492243","https://openalex.org/W2909877301","https://openalex.org/W2991307025","https://openalex.org/W3007077942","https://openalex.org/W3082687733","https://openalex.org/W3122207805","https://openalex.org/W6678540043","https://openalex.org/W6748102297"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"Time":[0],"series":[1,38,102],"anomaly":[2,28],"detection":[3,29],"is":[4,15,46,157],"getting":[5],"more":[6,8],"and":[7,40,73,120,141,148],"attention":[9],"in":[10,22,48,70],"IT":[11],"operations.":[12],"Deep":[13],"learning":[14],"one":[16],"of":[17,55,103,123,130,171],"the":[18,67,71,79,84,97,118,121,124,127,131,142,145,149,155,164,169,172],"most":[19],"used":[20],"technologies":[21],"recent":[23],"years,":[24],"which":[25],"realizes":[26],"automatic":[27],"on":[30,163],"raw":[31,98],"data.":[32],"A":[33],"novel":[34],"algorithm":[35,52,64,94],"combining":[36],"time":[37],"decomposition":[39,89],"Long":[41],"Short":[42],"Term":[43],"Memory":[44],"(LSTM)":[45],"proposed":[47,173],"this":[49],"paper.":[50],"This":[51],"mainly":[53],"consists":[54],"four":[56],"steps:":[57],"1)":[58],"we":[59,82,113,135],"use":[60,75,83,114,136],"a":[61,101,137],"density-based":[62],"clustering":[63],"to":[65,77,95,116,152],"remove":[66],"sharp":[68],"points":[69],"data,":[72],"then":[74],"interpolation":[76],"complete":[78,85],"data;":[80,133],"2)":[81],"ensemble":[86],"empirical":[87],"mode":[88,109],"with":[90],"adaptive":[91],"noise":[92],"(CEEMDAN)":[93],"decompose":[96],"data":[99],"into":[100],"relatively":[104],"simple":[105],"components,":[106],"called":[107],"intrinsic":[108],"functions":[110],"(IMFs);":[111],"3)":[112],"LSTM":[115],"predict":[117],"IMFs,":[119],"sum":[122],"results":[125,129,162],"represents":[126],"predicted":[128,150],"original":[132,146],"4)":[134],"dynamic":[138],"threshold":[139],"rule":[140],"error":[143],"between":[144],"value":[147,151],"determine":[153],"whether":[154],"point":[156],"an":[158],"anomaly.":[159],"The":[160],"experimental":[161],"Yahoo":[165],"Webscope_S5":[166],"dataset":[167],"prove":[168],"effectiveness":[170],"model.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
