{"id":"https://openalex.org/W7147282657","doi":"https://doi.org/10.1109/cnml68938.2026.11453190","title":"RFETR: Residual Frequency-Enhanced Two-stage Reconstruction for Time Series Anomaly Detection","display_name":"RFETR: Residual Frequency-Enhanced Two-stage Reconstruction for Time Series Anomaly Detection","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147282657","doi":"https://doi.org/10.1109/cnml68938.2026.11453190"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11453190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132632657","display_name":"Shilong Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Shilong Jing","raw_affiliation_strings":["University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China","institution_ids":["https://openalex.org/I4210164862"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132703149","display_name":"Jun Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jun Wu","raw_affiliation_strings":["University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China","institution_ids":["https://openalex.org/I4210164862"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132548586","display_name":"Pengfei Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Pengfei Zhan","raw_affiliation_strings":["University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China","institution_ids":["https://openalex.org/I4210164862"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132605731","display_name":"Tianfeng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164862","display_name":"Artificial Intelligence in Medicine (Canada)","ror":"https://ror.org/05p590m36","country_code":"CA","type":"company","lineage":["https://openalex.org/I4210164862"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Tianfeng Zhang","raw_affiliation_strings":["University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"University of Technology,School of Computer Science and Artificial Intelligence,Wuhan,China","institution_ids":["https://openalex.org/I4210164862"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5132632657"],"corresponding_institution_ids":["https://openalex.org/I4210164862"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94117471,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1101","last_page":"1105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9251999855041504,"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":0.9251999855041504,"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":0.045499999076128006,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.003100000089034438,"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/residual","display_name":"Residual","score":0.8546000123023987},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6599000096321106},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6204000115394592},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5408999919891357},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5181000232696533},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4512999951839447},{"id":"https://openalex.org/keywords/asymmetry","display_name":"Asymmetry","score":0.4350000023841858},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.4253999888896942},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.39430001378059387}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8546000123023987},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6599000096321106},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6204000115394592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5651999711990356},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.534500002861023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5181000232696533},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4668000042438507},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C38976095","wikidata":"https://www.wikidata.org/wiki/Q752641","display_name":"Asymmetry","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.4253999888896942},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.34369999170303345},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27149999141693115},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11453190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11453190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6252540349960327,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2950361482","https://openalex.org/W3081497074","https://openalex.org/W4283318673","https://openalex.org/W4385562572","https://openalex.org/W4396757586"],"related_works":[],"abstract_inverted_index":{"Reconstruction-based":[0],"methods":[1,21,38],"are":[2],"mainstream":[3],"paradigms":[4],"for":[5,34,117,127],"unsupervised":[6],"time":[7],"series":[8],"anomaly":[9,54,96,130],"detection.":[10],"However,":[11],"with":[12,110,120,143],"increasing":[13],"expressiveness":[14],"of":[15],"deep":[16],"models":[17,28],"like":[18],"Transformers,":[19],"these":[20,58],"face":[22],"the":[23,80],"over-generalization":[24],"problem":[25],"where":[26],"high-capacity":[27],"produce":[29],"low":[30],"reconstruction":[31,108,115],"errors":[32,116],"even":[33],"anomalies.":[35],"Additionally,":[36],"existing":[37],"exhibit":[39],"low-":[40],"frequency":[41,44,49],"bias":[42],"in":[43],"domain":[45],"modeling,":[46],"neglecting":[47],"high-":[48],"details":[50],"that":[51,78,113,138],"contain":[52],"discriminative":[53],"information.":[55],"To":[56],"address":[57],"issues,":[59],"we":[60],"propose":[61],"RFETR,":[62],"a":[63,73,105,121],"Residual":[64,74],"Frequency-Enhanced":[65],"Two-stage":[66],"Reconstruction":[67],"framework.":[68],"The":[69],"core":[70],"innovation":[71],"is":[72],"Frequency":[75],"Enhancement":[76],"mechanism":[77],"modulates":[79],"spectral":[81],"residual":[82],"between":[83],"original":[84,92],"and":[85,148],"reconstructed":[86],"sequences":[87],"rather":[88],"than":[89],"directly":[90],"masking":[91],"spectra,":[93],"enabling":[94],"adaptive":[95,123],"amplification":[97],"while":[98],"preserving":[99],"normal":[100],"patterns.":[101],"We":[102],"further":[103],"design":[104],"parameter-shared":[106],"two-stage":[107],"framework":[109],"input-target":[111],"asymmetry":[112],"amplifies":[114],"anomalies,":[118],"along":[119],"feature-driven":[122],"error":[124],"fusion":[125],"strategy":[126],"handling":[128],"diverse":[129],"types.":[131],"Experiments":[132],"on":[133],"five":[134],"public":[135],"datasets":[136],"demonstrate":[137],"RFETR":[139],"achieves":[140],"state-of-the-art":[141],"performance":[142],"90.0":[144],"percent":[145,150],"average":[146,151],"F1-score":[147],"94.7":[149],"AUC-ROC.":[152]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}