{"id":"W2128510565","doi":"10.1109/ifsa-nafips.2013.6608627","title":"Anomaly detection in time series data using a fuzzy c-means clustering","year":2013,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Anomaly detection; Series (stratigraphy); Cluster analysis; Anomaly (physics); Time series; Autocorrelation; Data mining; Pattern recognition (psychology); Representation (politics); Computer science; Subsequence; Fuzzy clustering; Artificial intelligence; Mathematics; Machine learning; Statistics; Geology","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":[0.001799842,0.000796902,0.0009812667,0.004055643,0.001244398,0.00128243,0.001397214,0.001300245,0.0007120126],"category_scores_gemma":[0.005354032,0.0003034787,0.001109837,0.003164061,0.00064268,0.001007009,0.0006590202,0.001118496,0.0003560929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058767,"about_ca_system_score_gemma":0.00152604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0152977,"about_ca_topic_score_gemma":0.009502838,"domain_scores_codex":[0.9984542,0.0002125621,0.0001470662,0.0004039418,0.0006950621,0.00008710083],"domain_scores_gemma":[0.9982627,0.0005695552,0.0001918473,0.0001616289,0.0007629672,0.00005140262],"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":[0.0004293483,0.000300166,0.00819532,0.0002991548,0.000311396,0.0003879816,0.0006460529,0.2709197,0.04624575,0.01178454,0.003048643,0.657432],"study_design_scores_gemma":[0.000007130153,0.00004140138,0.002588237,0.00001570482,0.00002784054,0.00008852483,0.00005862568,0.9844583,0.008841263,0.002882268,0.0009524621,0.00003821633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04177215,0.0002035887,0.9562693,0.00009722271,0.00004456987,0.00009824124,0.0001369172,0.0007993163,0.0005787255],"genre_scores_gemma":[0.2643469,0.0001896499,0.7341437,0.00002969395,0.00003087717,0.0001223569,0.0003599025,0.00006699305,0.0007099459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0152977,"threshold_uncertainty_score":0.03041732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02898698928399773,"score_gpt":0.2594748774082521,"score_spread":0.2304878881242544,"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."}}