{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007005268,0.001157964,0.001064191,0.002475483,0.0004074117,0.0007817288,0.001847001,0.000956694,0.001336966],"category_scores_gemma":[0.002690353,0.0003650699,0.001383521,0.002502055,0.000561759,0.001952727,0.001042414,0.001728326,0.0006813519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007589907,"about_ca_system_score_gemma":0.001074584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008623472,"about_ca_topic_score_gemma":0.009887923,"domain_scores_codex":[0.9993056,0.00008040909,0.00004688941,0.0002805472,0.0002091555,0.00007726896],"domain_scores_gemma":[0.9990558,0.0002806435,0.0001659423,0.0001378866,0.0002877419,0.00007203632],"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":[0.0002299026,0.0002242905,0.008879259,0.0001721626,0.0002796771,0.0003722947,0.000150195,0.1613171,0.02083273,0.01163345,0.01161987,0.7842891],"study_design_scores_gemma":[0.000006056795,0.0000203025,0.0007647648,0.000004190988,0.0000150426,0.00009467042,0.00001110508,0.9915418,0.001937246,0.004424448,0.00117072,0.000009701658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01682218,0.0003094081,0.9791326,0.0001944654,0.00007994564,0.00004478317,0.0002837619,0.002501649,0.0006311715],"genre_scores_gemma":[0.5184746,0.0006820299,0.4724558,0.0003843303,0.0002243173,0.0001666657,0.002588905,0.0005330741,0.004490327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008623472,"threshold_uncertainty_score":0.01714659,"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."}}