{"id":"https://openalex.org/W6959603642","doi":"https://doi.org/10.1109/tim.2025.3548177","title":"A Time Series Self-Supervised Contrastive Pretraining Method With Data Augmentation Using Discrepancy of Reconstruction Information Loss","display_name":"A Time Series Self-Supervised Contrastive Pretraining Method With Data Augmentation Using Discrepancy of Reconstruction Information Loss","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W6959603642","doi":"https://doi.org/10.1109/tim.2025.3548177"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2025.3548177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3548177","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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":null,"display_name":"Zhen Zhang","orcid":"https://orcid.org/0000-0001-6985-2103"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Zhang","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6985-2103","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yongming Han","orcid":"https://orcid.org/0000-0003-3209-725X"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongming Han","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3209-725X","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Ma","orcid":"https://orcid.org/0000-0003-1359-8622"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Ma","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1359-8622","affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zhiqiang Geng","orcid":"https://orcid.org/0000-0003-0647-3792"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Geng","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0647-3792","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":1.396,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87678561,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"74","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.4959000051021576,"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":0.4959000051021576,"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":0.2084999978542328,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.17389999330043793,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/series","display_name":"Series (stratigraphy)","score":0.6460999846458435},{"id":"https://openalex.org/keywords/aliasing","display_name":"Aliasing","score":0.612500011920929},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.605400025844574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5130000114440918},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4715999960899353},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.365200012922287},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.3458000123500824}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7297999858856201},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6460999846458435},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.612500011920929},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.605400025844574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5429999828338623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4715999960899353},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36730000376701355},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3637000024318695},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.3458000123500824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34529998898506165},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3125},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2793000042438507},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2025.3548177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3548177","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3833379496","display_name":null,"funder_award_id":"62203038","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7674376746","display_name":null,"funder_award_id":"62373035","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2620664872","https://openalex.org/W2786827964","https://openalex.org/W2950361482","https://openalex.org/W3008872739","https://openalex.org/W3107005520","https://openalex.org/W3125088505","https://openalex.org/W3133777397","https://openalex.org/W3175593095","https://openalex.org/W3184127157","https://openalex.org/W3190152617","https://openalex.org/W3199148273","https://openalex.org/W4213248681","https://openalex.org/W4313156423","https://openalex.org/W4382407495","https://openalex.org/W4387415039","https://openalex.org/W4388342097","https://openalex.org/W4390343450","https://openalex.org/W4390776918","https://openalex.org/W4393160493","https://openalex.org/W4400726538","https://openalex.org/W6739917289","https://openalex.org/W6759891849","https://openalex.org/W6774314701","https://openalex.org/W6778102432","https://openalex.org/W6778883912","https://openalex.org/W6811289536","https://openalex.org/W6838755211","https://openalex.org/W6838895826","https://openalex.org/W6848443141","https://openalex.org/W6849726903","https://openalex.org/W6850686174","https://openalex.org/W6855088300"],"related_works":[],"abstract_inverted_index":{"Self-supervised":[0],"pretraining":[1,72],"has":[2],"shown":[3],"considerable":[4],"advantages":[5],"in":[6,111,130],"multiple":[7],"time":[8,68,176],"series":[9,69,97,177],"tasks.":[10,183],"A":[11],"widely":[12],"used":[13],"class":[14],"of":[15,80,94],"methods":[16],"is":[17,26,140,191],"based":[18],"on":[19,165,170],"contrastive":[20,71],"learning":[21],"and":[22,58,91,125,167,181],"a":[23],"key":[24],"problem":[25],"how":[27],"to":[28,107,162,193],"construct":[29],"augmented":[30,49,86,132],"samples.":[31],"At":[32],"present,":[33],"explicit":[34,131],"augmentation":[35,77],"approaches":[36,133],"are":[37],"the":[38,54,95,117,126,155,195],"mainstream":[39],"for":[40,174],"constructing":[41],"positive":[42,87],"pairs":[43,88],"with":[44,74,89,116],"similar":[45],"features.":[46,61],"However,":[47],"explicitly":[48],"samples":[50],"may":[51],"deviate":[52],"from":[53],"original":[55,96],"data":[56,76],"space":[57],"have":[59],"aliasing":[60,127],"Therefore,":[62],"this":[63],"article":[64],"designs":[65],"an":[66],"end-to-end":[67],"self-supervised":[70],"network":[73],"implicit":[75],"using":[78,102,142],"discrepancy":[79],"reconstruction":[81],"information":[82,109],"loss":[83,110],"(CLR-DRL).":[84],"Specifically,":[85],"discrepancies":[90],"inherent":[92,120],"features":[93,121],"can":[98,122,147],"be":[99,123,135,149],"generated":[100],"automatically":[101],"different":[103,108],"autoencoders":[104],"(AE)":[105],"due":[106],"their":[112],"hidden":[113],"spaces.":[114],"Combined":[115],"joint":[118],"constraint,":[119],"extracted":[124],"impact":[128],"existing":[129],"could":[134],"mitigated.":[136],"Moreover,":[137],"theoretical":[138],"analysis":[139],"provided":[141,192],"gradient":[143],"flow":[144],"calculation,":[145],"which":[146],"also":[148],"confirmed":[150],"by":[151],"experiment":[152],"analysis.":[153],"Experimentally,":[154],"CLR-DRL":[156],"achieves":[157],"state-of-the-art":[158],"(SOTA)":[159],"performance":[160],"compared":[161],"current":[163],"baselines":[164],"in-":[166],"cross-domain":[168],"settings":[169],"nine":[171],"real-world":[172],"benchmarks":[173],"both":[175],"anomaly":[178],"detection":[179],"(AD)":[180],"classification":[182],"An":[184],"actual":[185],"pipeline":[186],"transportation":[187],"status":[188],"monitoring":[189],"case":[190],"verify":[194],"effectiveness.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}