{"id":"https://openalex.org/W4387171655","doi":"https://doi.org/10.3233/faia230422","title":"DeepDiscord: Dual Contrastive Coding for Transferable Time Series Anomaly Detection","display_name":"DeepDiscord: Dual Contrastive Coding for Transferable Time Series Anomaly Detection","publication_year":2023,"publication_date":"2023-09-28","ids":{"openalex":"https://openalex.org/W4387171655","doi":"https://doi.org/10.3233/faia230422"},"language":"en","primary_location":{"id":"doi:10.3233/faia230422","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia230422","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230422","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230422","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100370265","display_name":"Xinyi Li","orcid":"https://orcid.org/0000-0001-8241-7536"},"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":"Xin-Yi Li","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012152441","display_name":"Pei-Nan Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]},{"id":"https://openalex.org/I4210160618","display_name":"Huawei Technologies (United Kingdom)","ror":"https://ror.org/056gzgs71","country_code":"GB","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210160618"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Pei-Nan Zhong","raw_affiliation_strings":["General Development Dept, Huawei Technologies Co. Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"General Development Dept, Huawei Technologies Co. Ltd","institution_ids":["https://openalex.org/I2250955327","https://openalex.org/I4210160618"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100368449","display_name":"Di Chen","orcid":"https://orcid.org/0000-0002-0514-7947"},"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":"Di Chen","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079874042","display_name":"Zhendong Zhang","orcid":"https://orcid.org/0000-0002-5267-9030"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]},{"id":"https://openalex.org/I4210160618","display_name":"Huawei Technologies (United Kingdom)","ror":"https://ror.org/056gzgs71","country_code":"GB","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210160618"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Zhen-Dong Zhang","raw_affiliation_strings":["General Development Dept, Huawei Technologies Co. Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"General Development Dept, Huawei Technologies Co. Ltd","institution_ids":["https://openalex.org/I2250955327","https://openalex.org/I4210160618"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111673780","display_name":"Yu-Bin Yang","orcid":"https://orcid.org/0000-0002-3764-1114"},"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":true,"raw_author_name":"Yu-Bin Yang","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology, Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology, Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111673780"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.7082,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79132679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9987999796867371,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7451121211051941},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7152645587921143},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6055108904838562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5848439335823059},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5631384253501892},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39281532168388367},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37581923604011536}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7451121211051941},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7152645587921143},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6055108904838562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5848439335823059},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5631384253501892},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39281532168388367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37581923604011536},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia230422","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia230422","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230422","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia230422","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia230422","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230422","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G7549969683","display_name":null,"funder_award_id":"62176119","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387171655.pdf","grobid_xml":"https://content.openalex.org/works/W4387171655.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1442292319","https://openalex.org/W1522301498","https://openalex.org/W1788497923","https://openalex.org/W1970088130","https://openalex.org/W2113049098","https://openalex.org/W2407991977","https://openalex.org/W2584499795","https://openalex.org/W2593864460","https://openalex.org/W2728116991","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2792764867","https://openalex.org/W2803697594","https://openalex.org/W2892383154","https://openalex.org/W2896457183","https://openalex.org/W2911200746","https://openalex.org/W2917551568","https://openalex.org/W2920777619","https://openalex.org/W2948517885","https://openalex.org/W2950361482","https://openalex.org/W2950757722","https://openalex.org/W2951585248","https://openalex.org/W2963307331","https://openalex.org/W2971121529","https://openalex.org/W2988244882","https://openalex.org/W2998702515","https://openalex.org/W3005893373","https://openalex.org/W3014106621","https://openalex.org/W3046187700","https://openalex.org/W3091751937","https://openalex.org/W3092172514","https://openalex.org/W3094502228","https://openalex.org/W3098957257","https://openalex.org/W3099971460","https://openalex.org/W3106543020","https://openalex.org/W3109518304","https://openalex.org/W3123465393","https://openalex.org/W3127386226","https://openalex.org/W3153180875","https://openalex.org/W3155567600","https://openalex.org/W3169661950","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W3174543847","https://openalex.org/W3175091623","https://openalex.org/W3176509052","https://openalex.org/W3180222915","https://openalex.org/W3188872815","https://openalex.org/W3190152617","https://openalex.org/W3199148273","https://openalex.org/W3200258828","https://openalex.org/W3204263062","https://openalex.org/W4226362568","https://openalex.org/W4287116209"],"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],"anomaly":[2,18,61,156],"detection":[3,157],"has":[4],"attracted":[5],"extensive":[6],"research":[7],"attention":[8],"owing":[9],"to":[10,82,92,114],"its":[11],"real-world":[12],"applications.":[13],"Existing":[14],"deep":[15,49],"learning":[16,50,85,123,148],"based":[17,51,143],"detectors":[19],"usually":[20],"require":[21],"a":[22,47,59,94,137],"separate":[23],"training":[24,32],"phase":[25],"for":[26,100],"each":[27],"dataset.":[28],"However,":[29],"the":[30,38,72,80,87,108,129,165,169],"long":[31],"time":[33],"restricts":[34],"their":[35,115],"practicality":[36],"in":[37,86,90,117],"industry":[39],"use.":[40],"To":[41,71],"address":[42],"this":[43],"limitation,":[44],"we":[45],"propose":[46],"novel":[48,138],"discord":[52,88,118,134],"search":[53],"method":[54],"named":[55],"DeepDiscord,":[56],"which":[57,127],"is":[58,79,141],"multi-scale":[60],"detector":[62],"capable":[63],"of":[64,74,103,133,172],"directly":[65],"examining":[66],"unseen":[67],"datasets":[68],"after":[69],"pre-training.":[70],"best":[73],"our":[75,77,145],"knowledge,":[76],"study":[78],"first":[81],"introduce":[83],"contrastive":[84,125,147],"search,":[89,119],"order":[91],"provide":[93],"flexible":[95],"and":[96,120,131],"effective":[97],"similarity":[98],"measure":[99],"various":[101],"kinds":[102],"data.":[104],"We":[105,150],"innovatively":[106],"divide":[107],"data":[109],"into":[110],"two":[111],"categories":[112],"according":[113],"roles":[116],"combine":[121],"dual":[122,146],"with":[124],"learning,":[126],"improves":[128],"efficiency":[130],"efficacy":[132],"search.":[135],"Furthermore,":[136],"pretext":[139],"task":[140],"proposed":[142],"on":[144,154,168],"setting.":[149],"evaluate":[151],"DeepDiscord":[152,163],"comprehensively":[153],"five":[155,173],"benchmarks.":[158,174],"Experimental":[159],"results":[160,167],"show":[161],"that":[162],"achieves":[164],"state-of-the-art":[166],"four":[170],"out":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}