{"id":"W2051705420","doi":"10.1198/016214504000001510","title":"Diagnostic Checking in ARMA Models With Uncorrelated Errors","year":2005,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Uncorrelated; Mathematics; Statistics; Computer science; Econometrics; Algorithm","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":[0.03972089,0.001118692,0.00222595,0.005058018,0.001058343,0.002683533,0.003520709,0.003556174,0.002257536],"category_scores_gemma":[0.3245221,0.0008595904,0.001345819,0.00378244,0.003807823,0.003470481,0.003056047,0.002113468,0.0003223163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123982,"about_ca_system_score_gemma":0.002620981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002503327,"about_ca_topic_score_gemma":0.001593381,"domain_scores_codex":[0.9765622,0.01567374,0.001408542,0.002318698,0.003268011,0.0007687829],"domain_scores_gemma":[0.5486472,0.4181965,0.02021661,0.007083022,0.004362009,0.001494756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001241466,0.0003271117,0.1080739,0.0005431147,0.0006736645,0.003439422,0.0008453744,0.5256938,0.001828227,0.2192329,0.003484784,0.1346162],"study_design_scores_gemma":[0.000100337,0.0001205168,0.002662059,0.00004533973,0.00004845353,0.0003482415,0.00008412645,0.8676648,0.0009468113,0.1275042,0.0004283366,0.00004683611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1987042,0.0008173464,0.7957166,0.001328127,0.0001150779,0.00009807811,0.0003236718,0.001044782,0.001852157],"genre_scores_gemma":[0.9020801,0.0002626068,0.09624323,0.000220779,0.00008132982,0.0001043121,0.0004356364,0.00005296302,0.0005191211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03972089,"threshold_uncertainty_score":0.2100666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05216969875186943,"score_gpt":0.3776856446279548,"score_spread":0.3255159458760854,"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."}}