{"id":"W3121798988","doi":"10.1515/demo-2015-0005","title":"Forecasting time series with multivariate copulas","year":2015,"lang":"en","type":"article","venue":"Dependence Modeling","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Univariate; Copula (linguistics); Multivariate statistics; Series (stratigraphy); Econometrics; Independence (probability theory); Conditional independence; Marginal distribution; Time series; Tail dependence; Mathematics; Statistics; Computer science; Random variable; Geology","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":[0.002668572,0.0006531492,0.00107525,0.0009429514,0.00031607,0.001000597,0.0009244155,0.0007785451,0.001531537],"category_scores_gemma":[0.009782171,0.0003948287,0.0008620379,0.001094329,0.0003959585,0.001279743,0.0006261927,0.001682999,0.0004116006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005599919,"about_ca_system_score_gemma":0.0005573773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007666923,"about_ca_topic_score_gemma":0.004313062,"domain_scores_codex":[0.9991528,0.0003961929,0.0000421089,0.0001536264,0.0001982933,0.00005693673],"domain_scores_gemma":[0.9949048,0.003535829,0.0004669089,0.0004864669,0.0004897138,0.0001163198],"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":[0.00006909666,0.0000600301,0.002965132,0.0000285502,0.0000825,0.00006941521,0.00004080005,0.947122,0.001900241,0.008017302,0.0007073636,0.03893762],"study_design_scores_gemma":[0.000001121315,0.00000330489,0.0001390879,0.000001423082,0.00000189525,0.00000322937,0.000001087663,0.9986957,0.0001543076,0.0009540336,0.00004290611,0.000002076402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07434437,0.0002315625,0.9236892,0.0002196934,0.00004431773,0.00002340232,0.0001006374,0.0005885713,0.0007582919],"genre_scores_gemma":[0.8208557,0.000306376,0.1771837,0.00007964735,0.00009874627,0.00005261066,0.0002525651,0.0001166433,0.001054027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007666923,"threshold_uncertainty_score":0.0152446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1148607781257275,"score_gpt":0.2389235146994911,"score_spread":0.1240627365737636,"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."}}