{"id":"W3202688699","doi":"10.1109/access.2021.3114809","title":"Improving Stock Price Prediction Using Combining Forecasts Methods","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Yayasan UTP; Universiti Teknologi Petronas","keywords":"EWMA chart; Hilbert–Huang transform; Autoregressive integrated moving average; Computer science; Moving average; Exponential smoothing; Econometrics; Statistics; Time series; Machine learning; Mathematics; Control chart; Process (computing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002876738,0.0009232339,0.001209887,0.00231389,0.0003656603,0.001132898,0.001049981,0.0007502622,0.001228149],"category_scores_gemma":[0.008458759,0.0004327055,0.001072944,0.001696862,0.0002464426,0.001897767,0.001115938,0.0009243328,0.0005949101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003891061,"about_ca_system_score_gemma":0.0004979078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002808521,"about_ca_topic_score_gemma":0.002892822,"domain_scores_codex":[0.9983878,0.0004078718,0.0001163326,0.0002949598,0.0007074894,0.00008570641],"domain_scores_gemma":[0.9975553,0.001314694,0.0001898085,0.0002070518,0.0006658834,0.00006723539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001952933,0.0001956336,0.01115764,0.0001136345,0.0003010889,0.0001061541,0.0001261903,0.2784984,0.01004553,0.003163185,0.001658876,0.6944383],"study_design_scores_gemma":[0.00001096482,0.00005155458,0.001615618,0.000008905645,0.0000454523,0.00003328998,0.00001503829,0.9950675,0.001344549,0.001087433,0.000703569,0.00001609749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08801067,0.001059483,0.9057156,0.0002896107,0.0001907936,0.00008366583,0.0001108318,0.00109952,0.003439722],"genre_scores_gemma":[0.6670193,0.0006791917,0.329871,0.0001426576,0.0002614005,0.00008836299,0.0002649607,0.00007606505,0.001597142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002876738,"threshold_uncertainty_score":0.01521385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3285346427310327,"score_gpt":0.5174115662029913,"score_spread":0.1888769234719586,"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."}}