{"id":"W4315853251","doi":"10.3390/jrfm16010051","title":"Analysis of Bitcoin Price Prediction Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Econometrics; Regression; Linear regression; Regression analysis; Stock market; Computer science; Time series; Stock price; Lag; Stock market index; Statistics; Artificial intelligence; Machine learning; Economics; Series (stratigraphy); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006319622,0.00004940777,0.0001658109,0.0007848748,0.0001155214,0.00001544445,0.0002031831,0.00003950887,0.000001455778],"category_scores_gemma":[0.00003483842,0.00004349923,0.0000733701,0.002044613,0.00002502474,0.00008370174,0.0001523435,0.0001528524,5.956958e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000146525,"about_ca_system_score_gemma":0.000007915269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002785717,"about_ca_topic_score_gemma":0.000008854479,"domain_scores_codex":[0.9993803,0.00002946861,0.0002643399,0.0001003257,0.0001379368,0.00008766357],"domain_scores_gemma":[0.9994543,0.00003014814,0.0003072478,0.0001270777,0.00005805364,0.00002324057],"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":[0.00002822105,0.0001644227,0.2490218,0.00005551511,0.000476528,0.00005531246,0.001626816,0.02681285,0.00022682,0.09860726,0.000141297,0.6227832],"study_design_scores_gemma":[0.0002837228,0.00009672968,0.4013056,0.00001792075,0.0004119053,0.000006731378,0.00008333621,0.5815841,0.00007987429,0.005693912,0.01036903,0.00006718416],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4562606,0.0001752404,0.5433668,0.00005522892,0.0000509692,0.0000335962,0.000002674141,0.00002164642,0.00003325807],"genre_scores_gemma":[0.9861482,0.001660411,0.01214007,0.0000101717,0.00002020017,0.000001238759,8.700961e-7,0.000001933154,0.00001689418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.622716,"threshold_uncertainty_score":0.1773847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008354736826245173,"score_gpt":0.2246449396897761,"score_spread":0.2162902028635309,"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."}}