{"id":"W4403064440","doi":"10.1016/j.asoc.2024.112318","title":"Granular computing-based time series anomaly pattern detection with semantic interpretation","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Anomaly detection; Series (stratigraphy); Computer science; Anomaly (physics); Interpretation (philosophy); Time series; Granular computing; Pattern recognition (psychology); Artificial intelligence; Data mining; Algorithm; Machine learning; Geology; Programming language; Rough set; 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":[0.0008015824,0.0006209461,0.00108569,0.002328992,0.0005032754,0.00220812,0.0008913251,0.000575289,0.001122588],"category_scores_gemma":[0.003691394,0.0002820658,0.0009732683,0.002760699,0.0006737277,0.001747003,0.001181105,0.0009966276,0.0002810431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005852683,"about_ca_system_score_gemma":0.0007866945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003124089,"about_ca_topic_score_gemma":0.002222201,"domain_scores_codex":[0.9992769,0.00009129496,0.00008250392,0.0001777492,0.0002819763,0.00008951882],"domain_scores_gemma":[0.9988392,0.0003971482,0.0002008799,0.0002428268,0.0002615031,0.00005849827],"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":[0.001318831,0.0005499559,0.01490568,0.000449458,0.0003372991,0.0009874681,0.0005355261,0.1795135,0.04863421,0.07962466,0.005864226,0.6672792],"study_design_scores_gemma":[0.0000148821,0.00004771136,0.002188907,0.00001824108,0.00005083122,0.0001314246,0.00005978174,0.9560643,0.00449497,0.0359492,0.000960813,0.00001893163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07618257,0.0003484863,0.9189247,0.0002701787,0.0001056194,0.00007034158,0.0004175277,0.001655567,0.002025098],"genre_scores_gemma":[0.8347527,0.000208979,0.1635603,0.00007706282,0.00005950042,0.00005729674,0.0005637135,0.00007861033,0.0006418274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003124089,"threshold_uncertainty_score":0.006211817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004454668213334271,"score_gpt":0.1920182233411048,"score_spread":0.1875635551277705,"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."}}