{"id":"W2126368942","doi":"10.1109/nnsp.2002.1030013","title":"Metric-based model selection for time-series forecasting","year":2003,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Metric (unit); Computer science; Selection (genetic algorithm); Context (archaeology); Model selection; Series (stratigraphy); Feature selection; Time series; Machine learning; Artificial intelligence; Data mining; Task (project management); Feature (linguistics); Data modeling; Engineering","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":[0.0003996706,0.0001186974,0.0001582012,0.000165796,0.000272747,0.0001741886,0.0002274744,0.00004402938,0.00005995357],"category_scores_gemma":[0.0002286747,0.0001021194,0.0001282833,0.0008684213,0.00001402331,0.0005320556,0.00002876519,0.00004875172,0.00001455725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003515696,"about_ca_system_score_gemma":0.00007488425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009651194,"about_ca_topic_score_gemma":0.00002423369,"domain_scores_codex":[0.9990274,0.00002285752,0.0002120977,0.0002999247,0.0001478599,0.0002898353],"domain_scores_gemma":[0.9993957,0.00009964756,0.00009077782,0.0001912754,0.0001613569,0.00006124596],"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":[0.00002377365,0.0000942233,0.0006358289,0.00003802292,0.00007150979,0.000001015115,0.0001455042,0.5528198,0.002224501,0.3974681,0.003600431,0.04287734],"study_design_scores_gemma":[0.0001682911,0.00009168993,0.000003887079,0.000003626632,0.00001183594,0.000005589354,0.000008195214,0.9838354,0.009503826,0.003798886,0.00242898,0.0001397804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007449971,0.00002321276,0.9859131,0.0001330049,0.00004683227,0.0001213943,0.000001108839,0.0001662514,0.01285013],"genre_scores_gemma":[0.2558879,4.494451e-7,0.7389957,0.0001265081,0.00002220603,0.00002199533,0.000002452509,0.00001033673,0.004932406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4310156,"threshold_uncertainty_score":0.416431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03014453364498521,"score_gpt":0.2278416660385219,"score_spread":0.1976971323935366,"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."}}