{"id":"W2902727315","doi":"10.3390/jrfm11040086","title":"Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Global Financial Crisis and Policies","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Random forest; Wavelet transform; Wavelet; Currency; Stationary wavelet transform; Discrete wavelet transform; Artificial intelligence; Econometrics; Computer science; Pattern recognition (psychology); Mathematics; Statistics; Economics","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":[0.003001241,0.001001289,0.001532794,0.002132868,0.0003740937,0.001033457,0.001814471,0.001291873,0.0008490474],"category_scores_gemma":[0.004932059,0.000502562,0.001347182,0.001870976,0.0003958606,0.001538798,0.001049524,0.001399317,0.0005761783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003775197,"about_ca_system_score_gemma":0.0007165539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002932729,"about_ca_topic_score_gemma":0.003482299,"domain_scores_codex":[0.9989285,0.0004498301,0.00006373125,0.0002127887,0.0002372329,0.0001079962],"domain_scores_gemma":[0.9976276,0.001391611,0.0002608128,0.00020001,0.0004105212,0.0001095577],"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":[0.0002450022,0.0003891977,0.01166343,0.0001638484,0.0005835978,0.0002717458,0.00008264392,0.6553298,0.005336071,0.008873487,0.005172178,0.3118891],"study_design_scores_gemma":[0.000009732171,0.00002857475,0.0004017412,0.000007718726,0.00002346884,0.0000230374,0.000005542306,0.9938278,0.0003318501,0.004993487,0.0003390762,0.000008050445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02362228,0.0004522862,0.9739314,0.0003854596,0.0000913343,0.00004675746,0.0002309616,0.0006306979,0.0006089085],"genre_scores_gemma":[0.5538345,0.0006359181,0.4419285,0.0003768926,0.0005637368,0.0001721714,0.001181166,0.000145247,0.001161799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003001241,"threshold_uncertainty_score":0.0158723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017131399265257,"score_gpt":0.225631683897606,"score_spread":0.2054603699049534,"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."}}