{"id":"W4410887344","doi":"10.1109/syscon64521.2025.11014854","title":"Calibrated Unsupervised Anomaly Detection in Multivariate Time-Series Using Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Anomaly detection; Multivariate statistics; Series (stratigraphy); Reinforcement learning; Computer science; Artificial intelligence; Unsupervised learning; Anomaly (physics); Time series; Pattern recognition (psychology); Machine learning; Geology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001993964,0.0006327291,0.0007513296,0.0003945895,0.0002421052,0.0005996021,0.0008815402,0.0005773247,0.0004866111],"category_scores_gemma":[0.007604398,0.0003185406,0.0004698439,0.000252877,0.000971866,0.0009261986,0.0008000458,0.001325372,0.00009621932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007216643,"about_ca_system_score_gemma":0.0006775038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003029987,"about_ca_topic_score_gemma":0.002289186,"domain_scores_codex":[0.9993709,0.0002471085,0.00003182735,0.0001740463,0.0001108701,0.00006521542],"domain_scores_gemma":[0.9957644,0.002881266,0.0005242841,0.0002583611,0.000450117,0.0001215044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000723792,0.0000775779,0.002915271,0.0000266609,0.00004743699,0.00005031336,0.00005643671,0.9595504,0.003224566,0.003289842,0.0001775317,0.0305114],"study_design_scores_gemma":[0.000001582005,0.00001023326,0.0001195869,0.00000105986,0.000001193129,0.000003735455,0.00000127861,0.9988312,0.0003494848,0.0006594771,0.000019419,0.000001621802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1248462,0.0001078811,0.873832,0.0001500109,0.00002059872,0.00003269883,0.0000231304,0.0003975737,0.0005899408],"genre_scores_gemma":[0.9510301,0.00003767972,0.0483697,0.00003957015,0.00001431566,0.00003332325,0.00004191971,0.00002851928,0.0004048823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003029987,"threshold_uncertainty_score":0.01054525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01149448844109937,"score_gpt":0.2539032243105276,"score_spread":0.2424087358694282,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}