{"id":"W3094850776","doi":"10.33793/acperpro.03.01.129","title":"Comparison performance of Artificial Neural Networks and Fuzzy Inference systems in forecasting precious metals price Case Study: Gold, Silver, Platinum and Palladium","year":2020,"lang":"en","type":"article","venue":"Academic Perspective Procedia","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Palladium; Platinum; Precious metal; Artificial neural network; Liberian dollar; Purchasing; Artificial intelligence; Computer science; Chemistry; Metallurgy; Economics; Materials science; Finance; Metal; Catalysis; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002522901,0.001006294,0.0006686281,0.001260037,0.0004148376,0.0011747,0.0005439197,0.001119274,0.00104415],"category_scores_gemma":[0.006585703,0.000267655,0.0007747691,0.0007702964,0.0002124542,0.0009623097,0.0003197961,0.000828005,0.0002324773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008598521,"about_ca_system_score_gemma":0.0008133368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02692079,"about_ca_topic_score_gemma":0.01501599,"domain_scores_codex":[0.9991154,0.0002771699,0.0001025389,0.0001584448,0.0002325335,0.0001138823],"domain_scores_gemma":[0.9975166,0.001592397,0.0001640092,0.00007639972,0.0005998018,0.00005079524],"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":[0.001093506,0.0004842131,0.03019338,0.0005188363,0.0004853699,0.0002028292,0.0002685344,0.7588881,0.003268746,0.001626152,0.002242473,0.2007278],"study_design_scores_gemma":[0.00001630237,0.0002371847,0.004990637,0.00004779586,0.00008419822,0.00002295835,0.00009831459,0.9917911,0.001720093,0.0004811031,0.0004927838,0.00001761672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8544236,0.007009129,0.1168536,0.001360893,0.0005248872,0.0001484818,0.0005270865,0.0007210608,0.01843121],"genre_scores_gemma":[0.9853423,0.001123152,0.01154469,0.00007329846,0.00004261438,0.00004503341,0.0002737508,0.00001255954,0.001542651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02692079,"threshold_uncertainty_score":0.05352819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1883298672898591,"score_gpt":0.4149380520411837,"score_spread":0.2266081847513247,"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."}}