{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001405535,0.0007088451,0.0006393882,0.001143172,0.0002867999,0.000696747,0.0005075178,0.0005215486,0.001335712],"category_scores_gemma":[0.005112674,0.0001659695,0.0005547482,0.001097622,0.0002355682,0.001247177,0.0002818126,0.0007264823,0.0002605578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006705793,"about_ca_system_score_gemma":0.0006548055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0142184,"about_ca_topic_score_gemma":0.006040722,"domain_scores_codex":[0.9993681,0.0001258184,0.00004087181,0.0001303829,0.0002350862,0.00009976253],"domain_scores_gemma":[0.9975624,0.001551735,0.0002435016,0.0001100477,0.0004846941,0.00004750789],"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":[0.0002268447,0.000179087,0.06785747,0.0001236912,0.0001369434,0.0005127978,0.0000881734,0.8265774,0.003264221,0.003360335,0.001971703,0.09570136],"study_design_scores_gemma":[0.000001450389,0.00001157385,0.00340034,0.000003002804,0.000005001526,0.0000161373,0.000006644353,0.9956049,0.0004379721,0.0004310933,0.00007833273,0.000003507447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8712013,0.001012028,0.1217819,0.0007994968,0.00008167951,0.00004635765,0.0004489594,0.0008774678,0.003750828],"genre_scores_gemma":[0.9940627,0.0001506994,0.004911182,0.0000247366,0.00001793622,0.0000140289,0.0002900629,0.00001656967,0.000511988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0142184,"threshold_uncertainty_score":0.02827126,"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."}}