{"id":"W3083375849","doi":"10.1109/cec48606.2020.9185545","title":"Optimizing LSTM Based Network For Forecasting Stock Market","year":2020,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Mean squared error; Hyperparameter; Artificial neural network; Stock market; Artificial intelligence; Stock market prediction; Dropout (neural networks); Data mining; Machine learning; Statistics; Mathematics","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":[0.0003799525,0.0009538947,0.000557028,0.0003599123,0.0002725428,0.0006666919,0.0006535097,0.0009419125,0.00179248],"category_scores_gemma":[0.001059522,0.0003415348,0.0004123838,0.0004523245,0.0002514767,0.000884496,0.0003278458,0.0009711888,0.0002918115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000684623,"about_ca_system_score_gemma":0.0008137003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01147911,"about_ca_topic_score_gemma":0.01457608,"domain_scores_codex":[0.9998783,0.00002358015,0.000009328181,0.00003576331,0.00002725242,0.00002579012],"domain_scores_gemma":[0.9998446,0.00007786729,0.00001937052,0.000005955152,0.00004388473,0.000008302763],"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":[0.00007004987,0.00004524851,0.001000409,0.00006522451,0.00004820208,0.0000980314,0.00003346409,0.9450355,0.00340898,0.001373226,0.00131071,0.04751097],"study_design_scores_gemma":[0.000001768619,0.00000534702,0.00006155336,0.000001991506,0.000002725885,0.000003615084,0.000001826955,0.9992909,0.0002629939,0.0002904486,0.00007557753,0.000001257282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.232082,0.004365634,0.7491123,0.001077601,0.0003729549,0.00009159085,0.0004109549,0.002719939,0.009767125],"genre_scores_gemma":[0.9478862,0.0006953932,0.04703999,0.0001691598,0.00005773108,0.00008209092,0.0003551789,0.00006108012,0.003653174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01147911,"threshold_uncertainty_score":0.02282459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.288651198325565,"score_gpt":0.4018557098051408,"score_spread":0.1132045114795758,"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."}}