{"id":"W2080761477","doi":"10.1016/j.engappai.2014.12.015","title":"Fuzzy clustering of time series data using dynamic time warping distance","year":2015,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":275,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Technology Futures","keywords":"Dynamic time warping; Cluster analysis; Computer science; Fuzzy clustering; Data mining; Pattern recognition (psychology); Series (stratigraphy); Fuzzy logic; Time series; Artificial intelligence; Distance measures; Machine learning","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":[0.001218902,0.0005387715,0.0008035827,0.002910852,0.0008335575,0.001236275,0.0009898324,0.0007486038,0.000980621],"category_scores_gemma":[0.003894342,0.0002574746,0.001099093,0.002532172,0.0004679501,0.001234562,0.0007922533,0.0006618347,0.0003471584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009264669,"about_ca_system_score_gemma":0.0009601626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005695097,"about_ca_topic_score_gemma":0.00288604,"domain_scores_codex":[0.9989257,0.000190304,0.0001157694,0.000308532,0.0003814849,0.00007809589],"domain_scores_gemma":[0.998844,0.0003647431,0.0001274506,0.0001405626,0.0004768872,0.00004631131],"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":[0.0003922332,0.0001723938,0.002910428,0.0002230942,0.0002959264,0.0001671302,0.0005191025,0.3735617,0.02141798,0.02110853,0.002334231,0.5768973],"study_design_scores_gemma":[0.000007014852,0.00004928193,0.001429153,0.00001153978,0.00002549146,0.00004807228,0.00008119344,0.9858611,0.004023521,0.007426277,0.001012407,0.00002498196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05659013,0.0002544781,0.941678,0.00008910747,0.00005685051,0.00006354282,0.0001047281,0.0001943105,0.0009688081],"genre_scores_gemma":[0.5504022,0.0003476807,0.4464591,0.00003405961,0.00005858675,0.0001601692,0.0005966313,0.00008934393,0.001852206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005695097,"threshold_uncertainty_score":0.01132393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04550973745343292,"score_gpt":0.2776762159967628,"score_spread":0.2321664785433299,"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."}}