{"id":"W2345960201","doi":"10.15173/esr.v21i2.2769","title":"A \"HURST COEFFICIENT\" ESTIMATION WITH WAVELETS: APPLICATION TO THE ENERGY SECTOR","year":2015,"lang":"en","type":"article","venue":"Energy Studies Review","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hurst exponent; Wavelet; Futures contract; Econometrics; Herding; Economics; Long memory; Speculation; Maturity (psychological); Financial economics; Statistics; Computer science; Mathematics; Finance; Volatility (finance); Artificial intelligence","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":[0.001897934,0.0003615406,0.0004654124,0.001455073,0.0002322559,0.0008362366,0.0004270808,0.0006871717,0.0008685891],"category_scores_gemma":[0.008793187,0.0002065743,0.0006163524,0.001839619,0.0002129205,0.0008476231,0.000486077,0.0009669997,0.0003370032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429684,"about_ca_system_score_gemma":0.0003782216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913687,"about_ca_topic_score_gemma":0.0009326498,"domain_scores_codex":[0.9995671,0.0002079388,0.00003326852,0.00007129494,0.00008132328,0.00003911972],"domain_scores_gemma":[0.9976791,0.00154145,0.0002466911,0.0002328636,0.0002549062,0.00004514198],"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":[0.0003229394,0.0002424406,0.0517593,0.0003990821,0.000432969,0.0008129461,0.0004096943,0.2199009,0.01966635,0.03638598,0.003868078,0.6657994],"study_design_scores_gemma":[0.00001847943,0.00009742988,0.01299748,0.00004758489,0.00005680071,0.0001822185,0.0001267255,0.9721336,0.003205068,0.009021492,0.002080538,0.00003258867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2814251,0.001998564,0.7128979,0.0006326024,0.0001718728,0.00005217074,0.0003288079,0.0003491376,0.002143952],"genre_scores_gemma":[0.8313673,0.002403692,0.1642951,0.00004895425,0.0002150403,0.00004572168,0.0004356769,0.00009061297,0.001097856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001913687,"threshold_uncertainty_score":0.01003736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04784277433459834,"score_gpt":0.2626258552050136,"score_spread":0.2147830808704153,"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."}}