{"id":"W4312198614","doi":"10.1109/ieem55944.2022.9989798","title":"Bitcoin Data Analysis Using Deep Learning and Statistical Modeling","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Autoregressive integrated moving average; Recurrent neural network; Time series; Deep learning; Speculation; Artificial intelligence; Asset (computer security); Autoregressive conditional heteroskedasticity; Artificial neural network; Machine learning; Data modeling; Econometrics; Finance; Volatility (finance); Computer security; Economics","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":[0.0007616915,0.0007359048,0.0004553296,0.002007664,0.0002981443,0.0009814323,0.0005412294,0.0005482897,0.001956688],"category_scores_gemma":[0.003102756,0.0002078552,0.000503619,0.001889651,0.0003077037,0.001410148,0.0008135565,0.001229689,0.0006597026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005563322,"about_ca_system_score_gemma":0.0007926853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006004262,"about_ca_topic_score_gemma":0.006132527,"domain_scores_codex":[0.9995958,0.0000704192,0.00004382685,0.00008878403,0.0001450394,0.0000561638],"domain_scores_gemma":[0.9990672,0.0003265652,0.0001514654,0.0001461766,0.0002624473,0.00004605508],"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":[0.0003229484,0.0004252187,0.03607572,0.0002181984,0.0002118851,0.0008243539,0.0001882321,0.5352212,0.0214112,0.01990184,0.01309134,0.3721079],"study_design_scores_gemma":[0.000003122579,0.00001275669,0.002837535,0.000009558984,0.000004960252,0.00003879834,0.00002183766,0.9885741,0.002187417,0.005230865,0.001068376,0.00001067012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2417175,0.0008382203,0.740994,0.001294587,0.0001411699,0.0001525753,0.005004385,0.004701916,0.005155689],"genre_scores_gemma":[0.871178,0.0005124251,0.1191947,0.0001365433,0.00006975899,0.0001515612,0.005832041,0.0001771769,0.002747883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006004262,"threshold_uncertainty_score":0.01193863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1419642608441088,"score_gpt":0.2768152120522222,"score_spread":0.1348509512081134,"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."}}