{"id":"W2025446761","doi":"10.1145/2628194.2628207","title":"An experimental evaluation of similarity measures for uncertain time series","year":2014,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity (geometry); Computer science; Data mining; Benchmark (surveying); Heuristic; Probabilistic logic; Time series; Series (stratigraphy); Nearest neighbor search; Machine learning; Variable (mathematics); Sampling (signal processing); Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001457723,0.00006926057,0.000130109,0.00003685165,0.00009282463,0.00006283697,0.0002862385,0.00002755632,0.0001022687],"category_scores_gemma":[0.00007731019,0.00005671025,0.00006244158,0.0001251693,0.00002904014,0.0005130015,0.0000436179,0.00001856467,0.000003649994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002071851,"about_ca_system_score_gemma":0.00003125322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003733591,"about_ca_topic_score_gemma":0.00002201854,"domain_scores_codex":[0.9990813,0.0001020232,0.0001664847,0.000194759,0.0003406986,0.0001147226],"domain_scores_gemma":[0.9992815,0.00003468585,0.00007663569,0.0002994653,0.0002684686,0.00003925851],"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":[0.00009131568,0.0005161439,0.0008987109,0.00003475254,0.0001288378,2.369887e-7,0.003790733,0.0316107,0.2286386,0.3013875,0.001753685,0.4311487],"study_design_scores_gemma":[0.0001896999,0.0002528464,0.0001995036,0.000002933235,0.00001557236,7.387732e-7,0.0001023753,0.9166499,0.07849194,0.003342144,0.0006752645,0.00007710564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1432698,0.00008368439,0.8487754,0.0002759983,0.00008206745,0.0002687144,0.000003116928,0.00009481142,0.007146416],"genre_scores_gemma":[0.9294372,2.222388e-7,0.07033424,0.00004066038,0.00003618147,0.00001451036,0.000005877051,0.000003580464,0.0001275065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8850392,"threshold_uncertainty_score":0.2312577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05103785365693141,"score_gpt":0.3096191640672168,"score_spread":0.2585813104102854,"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."}}