{"id":"W4396816446","doi":"10.7763/ijcte.2024.v16.1353","title":"Adaptive Model Selection in Stock Market Prediction: A Modular and Scalable Big Data Analytics Approach","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Theory and Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Computer science; Scalability; Big data; Modular design; Stock market; Analytics; Data science; Data mining; Stock market prediction; Artificial intelligence; Machine learning; Database; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00141107,0.0009780439,0.0007843584,0.001029921,0.0004019812,0.001083693,0.001973717,0.0005749897,0.001470499],"category_scores_gemma":[0.003157024,0.0005721775,0.0006770887,0.0007085226,0.0004633975,0.002341309,0.0016348,0.001229665,0.0007040859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005502263,"about_ca_system_score_gemma":0.001057399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005988003,"about_ca_topic_score_gemma":0.00913512,"domain_scores_codex":[0.9994246,0.000132784,0.00003554543,0.0001661349,0.0001744572,0.00006660903],"domain_scores_gemma":[0.9987827,0.0004745692,0.0001168597,0.0003313744,0.0001972385,0.00009732287],"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":[0.0004663391,0.0004462275,0.009573868,0.0001413478,0.0003663273,0.0004517476,0.0002521437,0.5704968,0.01910139,0.009351707,0.006842207,0.3825099],"study_design_scores_gemma":[0.000008968288,0.00002837189,0.0003733115,0.000003320249,0.00001060491,0.00001937499,0.00001626286,0.9937407,0.001776451,0.003452946,0.0005630372,0.000006587932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04749423,0.000293654,0.9404116,0.0003762361,0.00006161384,0.0001141374,0.0001905604,0.009514662,0.001543265],"genre_scores_gemma":[0.6552181,0.0002104442,0.341297,0.0002107885,0.00008805695,0.0001841618,0.0006030268,0.000280788,0.001907544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005988003,"threshold_uncertainty_score":0.01190627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1194474821163626,"score_gpt":0.3376959792471385,"score_spread":0.2182484971307759,"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."}}