{"id":"W2149621509","doi":"10.1111/j.1467-9892.2005.00433.x","title":"Influence of Missing Values on the Prediction of a Stationary Time Series","year":2005,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Mathematics; Series (stratigraphy); Missing data; Statistics; Mean squared prediction error; Variance (accounting); Time series; Simple (philosophy); Long memory; Applied mathematics; Stationary process; Econometrics","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":[0.0456434,0.001174485,0.00187613,0.001000387,0.0009047566,0.002341707,0.001627921,0.00224525,0.001438145],"category_scores_gemma":[0.2390712,0.001002608,0.001172154,0.0009483656,0.002542287,0.003555238,0.001683391,0.004531597,0.0003239531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057953,"about_ca_system_score_gemma":0.001183343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00204995,"about_ca_topic_score_gemma":0.00122936,"domain_scores_codex":[0.989198,0.005547453,0.0007558041,0.001948192,0.001769918,0.0007807354],"domain_scores_gemma":[0.5434985,0.4157265,0.01625217,0.01531865,0.007288264,0.00191596],"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":[0.004616002,0.0003387868,0.1020183,0.0008335422,0.0008789364,0.00357757,0.0007515468,0.774015,0.006604274,0.02538084,0.002216307,0.07876889],"study_design_scores_gemma":[0.00008294782,0.0003762324,0.02142357,0.0001954219,0.0003570739,0.000821811,0.0001757424,0.9288468,0.009996171,0.03701745,0.0006088014,0.00009798122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5587186,0.002766302,0.4319073,0.002405044,0.0005108666,0.00006919468,0.0008355658,0.0008050819,0.001982049],"genre_scores_gemma":[0.9923752,0.000320699,0.006474663,0.0001057365,0.0001044616,0.00002498658,0.0002363484,0.00004164285,0.0003162526],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0456434,"threshold_uncertainty_score":0.2413883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554180177631767,"score_gpt":0.2137633356681422,"score_spread":0.1982215338918245,"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."}}