{"id":"W2914419771","doi":"10.1109/bigdata.2018.8622462","title":"A Comparative Study of LSTM and DNN for Stock Market Forecasting","year":2018,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Overfitting; Artificial neural network; Generalizability theory; Computer science; Artificial intelligence; Machine learning; Stock market; Econometrics; Recurrent neural network; Stock (firearms); Stock market index; Statistics; Economics; Mathematics; Engineering","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":[0.002294431,0.0006060058,0.0004068678,0.0009874197,0.0002489537,0.0008049652,0.0006829632,0.0009152043,0.001826569],"category_scores_gemma":[0.005074143,0.0001820732,0.0003226348,0.001087244,0.0001772329,0.001878477,0.0002753915,0.000657854,0.0003429946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167492,"about_ca_system_score_gemma":0.0006797201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01507736,"about_ca_topic_score_gemma":0.01736945,"domain_scores_codex":[0.9994111,0.0001951954,0.00005964603,0.00009523739,0.0001911803,0.00004757546],"domain_scores_gemma":[0.9979552,0.00129754,0.00009018266,0.00009062987,0.0005023889,0.00006403233],"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":[0.001493495,0.000274347,0.0121191,0.0006670772,0.0002817559,0.0002773713,0.0001724206,0.4316993,0.01219563,0.008787278,0.004224816,0.5278073],"study_design_scores_gemma":[0.00001986836,0.0001650538,0.002574031,0.0000409207,0.00003610812,0.00003899032,0.00005135847,0.990312,0.003560906,0.001638277,0.001547517,0.00001486642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7157355,0.02523986,0.211317,0.002770874,0.0008171035,0.0001689511,0.001384239,0.002224487,0.04034202],"genre_scores_gemma":[0.9219217,0.004822036,0.06780237,0.0002397942,0.0001449438,0.00004814103,0.0006782447,0.00007488881,0.004267952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01507736,"threshold_uncertainty_score":0.02997917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.389495939320603,"score_gpt":0.4865951196741332,"score_spread":0.09709918035353021,"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."}}