{"id":"W4382936032","doi":"10.23919/acc55779.2023.10156393","title":"Dynamic Probabilistic Latent Variable Model with Exogenous Variables for Dynamic Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Latent variable; Anomaly detection; Probabilistic logic; Computer science; Variable (mathematics); Markov process; Data mining; Hidden Markov model; Autoregressive model; Latent variable model; Machine learning; Artificial intelligence; Mathematics; Econometrics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001392086,0.0009903904,0.001006097,0.0008459783,0.000401518,0.001327077,0.002047885,0.0009294664,0.001823155],"category_scores_gemma":[0.003289171,0.0005212022,0.0009381718,0.001340541,0.0007249035,0.002047656,0.001137201,0.002320922,0.0005032418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007850848,"about_ca_system_score_gemma":0.001200586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006044344,"about_ca_topic_score_gemma":0.005324806,"domain_scores_codex":[0.9990164,0.0003212631,0.00004461469,0.0002910842,0.0002067634,0.0001197875],"domain_scores_gemma":[0.9990727,0.0004871032,0.0001872812,0.000084566,0.0001370962,0.00003134871],"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":[0.00009332583,0.00007762082,0.0037918,0.0001031061,0.000112599,0.0001194233,0.00009328446,0.8507673,0.001948963,0.08375743,0.001407665,0.05772758],"study_design_scores_gemma":[0.000003167213,0.000009765559,0.0002080537,0.000004190912,0.000009142207,0.00001262321,0.000004622771,0.9931979,0.0001758212,0.00581105,0.0005569236,0.000006754748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00426148,0.0001372558,0.9946434,0.00009404816,0.00003113541,0.00001345594,0.0000793934,0.0001587981,0.0005809033],"genre_scores_gemma":[0.7716199,0.001036585,0.2183714,0.0001771819,0.000171273,0.0003011076,0.00101644,0.0001385226,0.007167574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006044344,"threshold_uncertainty_score":0.01201832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007502156721765156,"score_gpt":0.196894926798115,"score_spread":0.1893927700763498,"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."}}