{"id":"https://openalex.org/W7130544780","doi":"https://doi.org/10.1109/ictc66702.2025.11387870","title":"An Empirical Analysis of Score Mapping in Time-Series Anomaly Detection","display_name":"An Empirical Analysis of Score Mapping in Time-Series Anomaly Detection","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7130544780","doi":"https://doi.org/10.1109/ictc66702.2025.11387870"},"language":null,"primary_location":{"id":"doi:10.1109/ictc66702.2025.11387870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11387870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","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/A5051763770","display_name":"Saejoon Park","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Saejoon Park","raw_affiliation_strings":["Chung-Ang University,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075009948","display_name":"G. Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gyuwon Lee","raw_affiliation_strings":["Chung-Ang University,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126366432","display_name":"Yunyong Ko","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yunyong Ko","raw_affiliation_strings":["Chung-Ang University,Seoul,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang University,Seoul,Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"907","last_page":"910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8866999745368958,"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":0.8866999745368958,"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":0.09000000357627869,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.0026000000070780516,"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/robustness","display_name":"Robustness (evolution)","score":0.6520000100135803},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.641700029373169},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.579200029373169},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4187999963760376},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/raw-score","display_name":"Raw score","score":0.4083999991416931},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.4009000062942505}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6520000100135803},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.641700029373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6018000245094299},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5123999714851379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49320000410079956},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4115000069141388},{"id":"https://openalex.org/C173633133","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw score","level":3,"score":0.4083999991416931},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.26980000734329224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc66702.2025.11387870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11387870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2743617586","https://openalex.org/W2768947629","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2911200746","https://openalex.org/W2950361482","https://openalex.org/W3081497074","https://openalex.org/W3128634608"],"related_works":[],"abstract_inverted_index":{"Time-Series":[0],"Anomaly":[1],"Detection":[2],"(TSAD)":[3],"is":[4],"a":[5,99],"critical":[6],"task":[7],"for":[8,148],"identifying":[9],"abnormal":[10,112],"patterns":[11],"in":[12,18],"sequential":[13],"data,":[14],"with":[15],"broad":[16],"applications":[17],"industrial":[19],"monitoring,":[20],"finance,":[21],"healthcare,":[22],"and":[23,77,111,120,144],"cybersecurity.":[24],"Although":[25],"many":[26],"TSAD":[27,64,153],"models":[28],"have":[29],"been":[30,35],"proposed,":[31],"little":[32],"attention":[33],"has":[34,98],"given":[36],"to":[37,49,128],"the":[38,58,69,107,122,138,150],"role":[39],"of":[40,44,60,140,152],"score":[41,61,83,96,142],"mapping\u2014the":[42],"transformation":[43],"raw":[45],"anomaly":[46],"scores":[47,113],"prior":[48],"threshold":[50],"determination.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55,67],"empirically":[56],"analyze":[57],"impact":[59],"mapping":[62,84,97,133,143],"on":[63,73,102],"performance.":[65],"Specifically,":[66],"train":[68],"state-of-the-art":[70],"model":[71],"(AnomalyTransformer)":[72],"two":[74],"real-world":[75],"datasets":[76],"evaluate":[78],"its":[79],"performance":[80,130],"under":[81,131],"different":[82],"strategies":[85],"using":[86],"multiple":[87],"metrics.":[88],"Our":[89],"study":[90],"reveals":[91],"three":[92],"key":[93],"findings:":[94],"(1)":[95],"substantial":[100],"influence":[101],"detection":[103],"performance;":[104],"(2)":[105],"enlarging":[106],"gap":[108],"between":[109],"normal":[110],"increases":[114],"precision":[115],"but":[116],"drastically":[117],"reduces":[118],"recall;":[119],"(3)":[121],"widely":[123],"used":[124],"point-adjust":[125],"metric":[126],"tends":[127],"overestimate":[129],"certain":[132],"strategies.":[134],"These":[135],"results":[136],"highlight":[137],"importance":[139],"proper":[141],"provide":[145],"practical":[146],"insights":[147],"improving":[149],"robustness":[151],"systems.":[154]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-20T00:00:00"}
