{"id":"W3091458940","doi":"10.1109/ijcnn48605.2020.9207537","title":"Deep Learning Based Approach for Fresh Produce Market Price Prediction","year":2020,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive integrated moving average; Computer science; Boosting (machine learning); Gradient boosting; Artificial intelligence; Deep learning; Machine learning; Convolutional neural network; Artificial neural network; Simple (philosophy); Predictive modelling; Recurrent neural network; Time series; Random forest","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":[0.0003651184,0.0005488189,0.0004263649,0.0005548645,0.0001351734,0.0006574926,0.0008260608,0.0007259771,0.002135769],"category_scores_gemma":[0.0007152781,0.0002686851,0.0004339861,0.0006217868,0.0001733057,0.00108711,0.0004138416,0.001028837,0.0005419398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007419469,"about_ca_system_score_gemma":0.0007460748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009914634,"about_ca_topic_score_gemma":0.009596912,"domain_scores_codex":[0.9998661,0.00001876989,0.000008975567,0.00003463062,0.00003981966,0.00003166035],"domain_scores_gemma":[0.9997947,0.0000755066,0.00003100345,0.0000154465,0.00007068238,0.00001270316],"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":[0.0001830576,0.0002671643,0.004577682,0.0001025914,0.000129388,0.000131535,0.00003011168,0.7499585,0.007084358,0.005650546,0.003419782,0.2284654],"study_design_scores_gemma":[0.00000190399,0.00001104942,0.0002869351,0.000001856999,0.000004750435,0.000004253956,0.000001976567,0.9978893,0.0006227182,0.0009621404,0.000210701,0.000002283618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1982925,0.002179653,0.787683,0.001083007,0.0001818875,0.00006026607,0.001015963,0.002171554,0.007332135],"genre_scores_gemma":[0.9274472,0.0006727014,0.06326636,0.0001957626,0.00007767014,0.00004507593,0.0009335113,0.00003994069,0.007321781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009914634,"threshold_uncertainty_score":0.01971388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1395051611571926,"score_gpt":0.3649599751006233,"score_spread":0.2254548139434307,"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."}}