{"id":"W4200079396","doi":"10.18280/ria.350606","title":"A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Backpropagation; Stock (firearms); Artificial neural network; Econometrics; Computer science; Singular spectrum analysis; Profit (economics); Financial market; Stock market index; Stock market; Economics; Financial economics; Artificial intelligence; Finance; Engineering; Microeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0005848335,0.0007903667,0.0009011629,0.0006996778,0.0003904332,0.0008192764,0.001122314,0.001068283,0.001144514],"category_scores_gemma":[0.0008099944,0.0003908143,0.0008569575,0.0007058342,0.0003466908,0.001171401,0.000521432,0.0006934009,0.0003005711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103997,"about_ca_system_score_gemma":0.0007768735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01711782,"about_ca_topic_score_gemma":0.01059931,"domain_scores_codex":[0.9997495,0.00004430889,0.00002201354,0.00005985567,0.00009006418,0.00003415297],"domain_scores_gemma":[0.9998062,0.00007349671,0.00001784373,0.000009682863,0.00008217093,0.00001066687],"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":[0.0001052424,0.0001010432,0.001446574,0.00007219287,0.0001155048,0.000110857,0.00004679682,0.9030102,0.00430463,0.001487405,0.0006985108,0.08850103],"study_design_scores_gemma":[0.000002587308,0.00001120703,0.0001098429,0.00000198826,0.000007086227,0.00000544369,0.000001294391,0.9993655,0.0002105717,0.0001969676,0.00008489644,0.000002703533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09297868,0.001644846,0.8985633,0.0003450147,0.0002157906,0.00008427464,0.0001060092,0.001146729,0.004915251],"genre_scores_gemma":[0.9306851,0.0008231438,0.06207633,0.0001218219,0.0001089381,0.0001295157,0.0001752701,0.00004234881,0.005837554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01711782,"threshold_uncertainty_score":0.0340364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04953438294427867,"score_gpt":0.2694688015616831,"score_spread":0.2199344186174044,"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."}}