{"id":"W2031662655","doi":"10.1016/j.spl.2009.06.017","title":"On multiplicative seasonal modelling for vector time series","year":2009,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Autocovariance; Mathematics; Series (stratigraphy); Multiplicative function; Residual; Autoregressive model; Applied mathematics; Statistics; Monte Carlo method; Autoregressive–moving-average model; Algorithm; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004897909,0.0002135089,0.0003902747,0.00007352573,0.0002011856,0.00006383162,0.0001823679,0.00008067548,0.00005257579],"category_scores_gemma":[0.0004834162,0.0002515951,0.0001158498,0.00010877,0.00009428636,0.000167058,0.00001807442,0.0001760853,0.000159688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835728,"about_ca_system_score_gemma":0.00002651856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004089381,"about_ca_topic_score_gemma":0.000007474238,"domain_scores_codex":[0.9983703,0.00001906026,0.0005552702,0.0006064179,0.00006155929,0.0003873373],"domain_scores_gemma":[0.998978,0.0002918493,0.0002051316,0.0003581234,0.00008137614,0.0000855638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001847435,0.0001225659,0.0004673688,0.00003893828,0.00001624885,7.629075e-7,0.0004601817,0.007615746,0.0001211225,0.987882,0.00164661,0.001443667],"study_design_scores_gemma":[0.0002997872,0.0002105753,0.002201133,0.00001256988,0.000005174541,2.829157e-7,0.000001026479,0.278253,0.0000476994,0.7173955,0.001349026,0.0002242106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0891941,0.00006016776,0.9039986,0.003076526,0.00009952508,0.0006728637,0.002511297,0.00006073062,0.0003262032],"genre_scores_gemma":[0.4452462,0.000008572452,0.5529346,0.001308881,0.000115813,0.00007251348,0.0001540785,0.00002477081,0.0001344827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3560521,"threshold_uncertainty_score":0.9999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268364428146814,"score_gpt":0.2316553560160069,"score_spread":0.1989717117345387,"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."}}