{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009166616,0.001006582,0.001194037,0.003331991,0.0006839911,0.001098937,0.001428193,0.001401713,0.001218639],"category_scores_gemma":[0.05479368,0.0002266549,0.0007308105,0.003287425,0.001043195,0.002526442,0.001370738,0.001132977,0.0002497306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009687043,"about_ca_system_score_gemma":0.0005808079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001970921,"about_ca_topic_score_gemma":0.001468673,"domain_scores_codex":[0.9922449,0.003046794,0.001108736,0.0008581583,0.0024739,0.0002675522],"domain_scores_gemma":[0.9529994,0.03414959,0.002785712,0.004558516,0.004768094,0.0007387712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005315796,0.00288264,0.01844607,0.002932343,0.000704643,0.0004215301,0.0006960984,0.5416125,0.03530966,0.01603655,0.005668746,0.3699734],"study_design_scores_gemma":[0.0001908561,0.003291102,0.01085863,0.0001040286,0.0001106976,0.0003013877,0.0003854784,0.9556374,0.02098036,0.006118668,0.001937528,0.00008379338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8321958,0.004355093,0.1538115,0.0007131641,0.0006281793,0.0006056367,0.001464244,0.0008615365,0.005364836],"genre_scores_gemma":[0.9077865,0.0006127345,0.08907267,0.00007682543,0.00009578474,0.0002392111,0.001623086,0.00005243598,0.0004407586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009166616,"threshold_uncertainty_score":0.04847825,"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."}}