{"id":"W2000692338","doi":"10.1007/s00500-014-1311-z","title":"Description and classification of granular time series","year":2014,"lang":"en","type":"article","venue":"Soft Computing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Granular computing; Exploit; Series (stratigraphy); Computer science; Classifier (UML); Parametric statistics; Artificial intelligence; Feature vector; Process (computing); Feature (linguistics); Time series; Realization (probability); Data mining; Machine learning; Mathematics; Rough set","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003982137,0.000068847,0.0001366401,0.0000573533,0.0001312865,0.00008811952,0.0001783551,0.0000284171,0.00000355962],"category_scores_gemma":[0.00006265022,0.0000651211,0.00003594431,0.0002036226,0.0000462756,0.0003108747,0.0001279804,0.00004705516,0.00001024961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007356264,"about_ca_system_score_gemma":0.00000719288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008859758,"about_ca_topic_score_gemma":0.000001452446,"domain_scores_codex":[0.9993217,0.00004834115,0.000203527,0.0001927435,0.0001117084,0.0001219546],"domain_scores_gemma":[0.9994932,0.00005547957,0.0001480619,0.0001996551,0.0000710696,0.00003256795],"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":[0.000008821072,0.00004706276,0.01643554,0.00008505733,0.00003106703,9.598754e-7,0.002020284,0.001391535,0.05936366,0.1561984,0.0002310232,0.7641866],"study_design_scores_gemma":[0.0000888361,0.00006007561,0.02458087,0.00002402871,0.000007456652,0.000007738835,0.00002879529,0.9707468,0.0005894803,0.002767029,0.001013152,0.00008575466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2105017,0.00004962298,0.7881305,0.0001516175,0.00005119256,0.00002987883,2.020109e-7,0.00007028082,0.001015079],"genre_scores_gemma":[0.9452974,0.00000161261,0.05454468,0.0000240529,0.00004424172,3.36521e-7,0.000002037549,0.000004048408,0.00008154489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9693552,"threshold_uncertainty_score":0.2655562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442102200374729,"score_gpt":0.2058611363462951,"score_spread":0.1914401143425478,"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."}}