{"id":"W2059335753","doi":"10.1007/s00180-014-0543-9","title":"Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns","year":2014,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"School of Natural Sciences, Mathematics, and Engineering, California State University, Bakersfield; University of British Columbia","keywords":"Autoregressive–moving-average model; Benchmark (surveying); Support vector machine; Artificial neural network; Computer science; Econometrics; Regression; Moving average; Artificial intelligence; Mathematics; Autoregressive model; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00355349,0.001006359,0.001702367,0.0007601611,0.0004702752,0.001460546,0.001605629,0.002102973,0.002387657],"category_scores_gemma":[0.01881937,0.0008074517,0.001045555,0.001207509,0.0008414128,0.002041269,0.001019668,0.002745931,0.0007275885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005953966,"about_ca_system_score_gemma":0.00127787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004926392,"about_ca_topic_score_gemma":0.004957329,"domain_scores_codex":[0.9990819,0.0003671105,0.00009750802,0.0001878305,0.0002147542,0.00005086407],"domain_scores_gemma":[0.9890404,0.008731213,0.0006447667,0.0004529903,0.0009931228,0.0001375962],"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":[0.0001299546,0.0001109494,0.001021464,0.000179029,0.0001208071,0.0001353875,0.0001100527,0.838904,0.002606082,0.03386308,0.001766526,0.1210526],"study_design_scores_gemma":[0.000001790579,0.000004510479,0.0000270436,0.000001871524,0.000003068005,0.000002878086,0.000001263297,0.998081,0.00008996894,0.001719942,0.0000645314,0.000002195701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01588943,0.0008822987,0.9820192,0.0003287936,0.00008542969,0.00001585016,0.00004856277,0.0002972017,0.000433197],"genre_scores_gemma":[0.5985513,0.002237997,0.3900819,0.0002107529,0.0005732668,0.0002641929,0.0005535512,0.000269397,0.007257751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004926392,"threshold_uncertainty_score":0.01879293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1644490461666072,"score_gpt":0.4292690607781053,"score_spread":0.2648200146114981,"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."}}