{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005048266,0.001229395,0.002040743,0.001385255,0.0005428761,0.0008188264,0.001217002,0.001107093,0.0008852906],"category_scores_gemma":[0.01184209,0.0004677976,0.0009179788,0.001911397,0.0005387736,0.001591431,0.001020322,0.001675772,0.0004333862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007412798,"about_ca_system_score_gemma":0.000854081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003095224,"about_ca_topic_score_gemma":0.002665619,"domain_scores_codex":[0.9972224,0.001703469,0.0001349463,0.0002909338,0.00057198,0.00007637121],"domain_scores_gemma":[0.9950459,0.003152557,0.0002889176,0.0005959659,0.0007623538,0.0001542919],"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":[0.0001949581,0.0001514418,0.002152036,0.0001400203,0.0002826574,0.0001036625,0.0001055283,0.6535745,0.004134193,0.01579549,0.004802881,0.3185627],"study_design_scores_gemma":[0.000008754851,0.0000402103,0.0002865839,0.000004139701,0.000009518592,0.00001715377,0.000004642842,0.9891455,0.0006972062,0.00913234,0.0006441392,0.000009816617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01004945,0.00110635,0.9873569,0.0001956934,0.00005949922,0.0000399705,0.000101665,0.0006955659,0.0003949658],"genre_scores_gemma":[0.4446596,0.001344753,0.550235,0.0002014474,0.0002675946,0.0003832174,0.001281176,0.0001838549,0.001443475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005048266,"threshold_uncertainty_score":0.02669805,"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."}}