{"id":"W4386806325","doi":"10.1007/978-3-031-43430-3_15","title":"Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Autoencoder; Anomaly detection; Artificial intelligence; Data mining; Key (lock); Graph; Adjacency list; Heuristic; Time series; Task (project management); Machine learning; Pattern recognition (psychology); Deep learning; Algorithm; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004352889,0.0003778966,0.0002992115,0.0007819424,0.0004711342,0.0004002612,0.0009050155,0.0002742381,0.000002842058],"category_scores_gemma":[0.00001471265,0.0003351066,0.00005615488,0.001250975,0.000210334,0.0007633317,0.0005822199,0.0004099851,0.00005395326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000158593,"about_ca_system_score_gemma":0.0001144785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009355145,"about_ca_topic_score_gemma":0.0002560217,"domain_scores_codex":[0.9973367,0.00002441965,0.0003674477,0.001383732,0.0005347286,0.0003529948],"domain_scores_gemma":[0.9984036,0.00009611998,0.0001938551,0.0009023088,0.0002341994,0.0001698862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003947785,0.00006292368,0.0005043042,0.00006917737,0.00003025209,0.00001528568,0.001080039,0.008230102,0.01645998,0.0112145,0.00003355856,0.9622604],"study_design_scores_gemma":[0.0003438026,0.001143321,0.003595781,0.0002426784,0.00002283364,0.000113834,0.000001177088,0.9150026,0.013981,0.06106603,0.003632738,0.0008542074],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00132335,0.00002105744,0.9955331,0.0006741819,0.0001518232,0.000952471,0.00001083324,0.001164307,0.0001688739],"genre_scores_gemma":[0.4406532,0.00002827532,0.5582873,0.0002723249,0.0001796239,0.000188252,0.00001241808,0.00005484635,0.0003237058],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9614062,"threshold_uncertainty_score":0.9999101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006529374539151251,"score_gpt":0.2113251535062311,"score_spread":0.2047957789670799,"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."}}