{"id":"W4366828973","doi":"10.3390/math11091985","title":"Stock Price Prediction Using CNN-BiLSTM-Attention Model","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Stock (firearms); Stock price; Recurrent neural network; Deep learning; Stock market index; Artificial neural network; Machine learning; Pattern recognition (psychology); Stock market; Series (stratigraphy)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007985817,0.0001634297,0.0002928193,0.0004737265,0.0002476146,0.0002073234,0.0005130169,0.0001229949,0.0001315487],"category_scores_gemma":[0.009113184,0.0001307538,0.0001381976,0.001844746,0.00005498851,0.0002842038,0.0002439939,0.0001628888,0.0004088252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008913764,"about_ca_system_score_gemma":0.00005791275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002790195,"about_ca_topic_score_gemma":0.000001056193,"domain_scores_codex":[0.996662,0.0001941949,0.0008161507,0.0004082321,0.001576968,0.0003424672],"domain_scores_gemma":[0.9965869,0.001876308,0.0003898831,0.0007535635,0.0002947723,0.00009860425],"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":[0.0001246274,0.001072206,0.03468003,0.000740797,0.0002567463,0.00005876232,0.02218413,0.4126162,0.08186382,0.02191439,0.125364,0.2991243],"study_design_scores_gemma":[0.0001235919,0.00001823824,0.002051112,0.00004416773,0.00002012874,0.00001520649,0.000421451,0.8323548,0.0001167649,0.1645481,0.0001827918,0.0001036678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4480266,0.000005568461,0.5443757,0.00006359933,0.0003632993,0.0002144669,0.00001177439,0.0002381247,0.006700845],"genre_scores_gemma":[0.3121915,0.000004436002,0.6699277,0.00005890434,0.0002153384,0.00004057159,0.000008456142,0.00006952768,0.0174836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4197386,"threshold_uncertainty_score":0.9992335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3669801181344376,"score_gpt":0.4576636418749997,"score_spread":0.09068352374056216,"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."}}