{"id":"W4408784152","doi":"10.1007/s44443-025-00024-3","title":"A novel anomaly detection method for multivariate time series based on spatial-temporal graph learning","year":2025,"lang":"en","type":"article","venue":"Journal of King Saud University - Computer and Information Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Major Science and Technology Projects in Yunnan Province","keywords":"Anomaly detection; Multivariate statistics; Series (stratigraphy); Computer science; Graph; Anomaly (physics); Time series; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Geology; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007471681,0.00007489517,0.000121279,0.0006790134,0.0006241936,0.0002483865,0.0003282399,0.00004184934,0.000001383032],"category_scores_gemma":[0.00002473698,0.00006758604,0.00007627229,0.0006354484,0.00005833417,0.002669162,0.00007196709,0.0001094496,7.18273e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003998883,"about_ca_system_score_gemma":0.00009930171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001042101,"about_ca_topic_score_gemma":0.000006465118,"domain_scores_codex":[0.99936,0.00004442729,0.0002282553,0.0001042466,0.0001659879,0.00009707385],"domain_scores_gemma":[0.9991428,0.0001401941,0.0003649184,0.00007367741,0.0002383206,0.0000400667],"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":[0.0001797137,0.00009660849,0.001156361,0.00006116131,0.00004927873,0.000001403927,0.001135979,0.0690596,0.003263574,0.04633868,0.0002787962,0.8783789],"study_design_scores_gemma":[0.0004211474,0.0005102041,0.004717115,0.00004480155,0.000008833101,0.00001504621,0.00007286546,0.9687049,0.001541492,0.0004813268,0.02340204,0.00008021194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002068682,0.000002422727,0.9963491,0.0007694487,0.0001334919,0.0001086486,0.000001701641,0.00005406784,0.0005124198],"genre_scores_gemma":[0.4110474,0.00000491435,0.5885746,0.0002938174,0.00002416546,5.492208e-7,7.373594e-7,9.902494e-7,0.00005279934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8996453,"threshold_uncertainty_score":0.4800856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009271093367766232,"score_gpt":0.2448203633149212,"score_spread":0.235549269947155,"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."}}