{"id":"W2911608625","doi":"10.1109/ssci.2018.8628641","title":"Hybrid Deep Learning Model for Stock Price Prediction","year":2018,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Deep learning; Time series; Machine learning; Mean squared error; Random forest; Stock price; Stock (firearms); Regression; Recurrent neural network; Econometrics; Series (stratigraphy); Statistics; 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":[0.0004747126,0.0005031651,0.0005990892,0.0005095257,0.0001812886,0.0006541658,0.001126263,0.0008102611,0.00236564],"category_scores_gemma":[0.0008018833,0.0002883347,0.0004718106,0.000622809,0.0002154024,0.001112215,0.0005786314,0.000960184,0.0005659995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006133585,"about_ca_system_score_gemma":0.0006528872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009635859,"about_ca_topic_score_gemma":0.009857138,"domain_scores_codex":[0.9997895,0.00003199971,0.00001551489,0.00006068639,0.00007022585,0.00003209038],"domain_scores_gemma":[0.9997446,0.0001005572,0.00002787502,0.00002104639,0.00009251699,0.00001338346],"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":[0.0001196167,0.0001068172,0.00149713,0.0000634594,0.00008257161,0.00008497663,0.00002936646,0.9100627,0.003012004,0.007668017,0.001927451,0.07534585],"study_design_scores_gemma":[0.00000227005,0.000006668218,0.00007387535,0.00000129041,0.000003132331,0.000004368356,7.711386e-7,0.9988237,0.0001734776,0.0007736206,0.0001350219,0.000001787527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07443332,0.001502119,0.9155071,0.0006269644,0.0001779784,0.00004555309,0.0006298993,0.001664879,0.005412088],"genre_scores_gemma":[0.9096465,0.0006750947,0.07730636,0.0002537641,0.00008353977,0.0001279652,0.000789356,0.00005627599,0.0110611],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009635859,"threshold_uncertainty_score":0.01915956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1639779155678442,"score_gpt":0.4171452619080596,"score_spread":0.2531673463402154,"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."}}