{"id":"W4353081850","doi":"10.1007/s11063-023-11239-8","title":"Model Selection for Time Series Forecasting An Empirical Analysis of Multiple Estimators","year":2023,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Canada Research Chairs","keywords":"Estimator; Computer science; Model selection; Selection (genetic algorithm); Series (stratigraphy); Time series; Set (abstract data type); Process (computing); Data mining; Machine learning; Econometrics; Statistics; 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":[0.0008287992,0.0001241642,0.0002979012,0.0006097674,0.0003437078,0.0001709427,0.000358318,0.0000561783,0.000008045058],"category_scores_gemma":[0.001056246,0.0001003874,0.0001595968,0.003726583,0.0001031526,0.0005092627,0.00005600557,0.0000865624,0.000005407623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002198434,"about_ca_system_score_gemma":0.00003860906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001217139,"about_ca_topic_score_gemma":0.00002412541,"domain_scores_codex":[0.9983235,0.00003618843,0.000486895,0.0004339772,0.0004577306,0.0002617096],"domain_scores_gemma":[0.9987357,0.0004346461,0.0002934465,0.0002251522,0.0002433105,0.00006774409],"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":[0.00006727044,0.00004273802,0.04209952,0.00002937942,0.00004867452,8.289706e-7,0.001202654,0.8012803,0.07352434,0.00003724936,0.01152737,0.07013965],"study_design_scores_gemma":[0.00006831597,0.00004232415,0.001954384,0.000008244561,0.00008425472,0.000002898389,0.00004989092,0.9938617,0.001688369,0.002033512,0.0000972299,0.000108922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8087308,0.000001769639,0.1887636,0.001981394,0.00001826467,0.0001564788,0.0000251461,0.0003019725,0.00002060533],"genre_scores_gemma":[0.9223097,1.298535e-7,0.07705674,0.0003348023,0.00003453752,0.00006233935,0.00004114786,0.00001743278,0.0001431943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1925813,"threshold_uncertainty_score":0.4093679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2529386865019005,"score_gpt":0.4301536047619929,"score_spread":0.1772149182600923,"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."}}