{"id":"W7061618823","doi":"","title":"Resampling in neural networks with application to financial time series","year":2000,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Gyrotron and Vacuum Electronics Research","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Stability (learning theory); Time series; Resampling; Nonlinear system; Term (time); Exchange rate; Series (stratigraphy); Data set","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223961,0.0001407764,0.000220613,0.0001231701,0.0001805744,0.00001753267,0.0004188444,0.00007951701,0.0003117411],"category_scores_gemma":[0.000001415496,0.0001333365,0.00007621438,0.0004116597,0.0000349685,0.00009270004,0.000037623,0.0003992066,0.00004507155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004623484,"about_ca_system_score_gemma":0.0001526381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001549608,"about_ca_topic_score_gemma":0.001438051,"domain_scores_codex":[0.9991795,0.00004650206,0.0000964243,0.0002233201,0.0001713191,0.0002828712],"domain_scores_gemma":[0.9994923,0.00002977798,0.00009578103,0.0002571455,0.00007289975,0.00005204022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.04142006,0.001039511,0.01488143,0.0004465269,0.0007687563,0.00004063962,0.02496297,0.3581751,0.05400496,0.03392064,0.01494846,0.4553909],"study_design_scores_gemma":[0.005079619,0.001756074,0.7885038,0.0004554843,0.0004896441,0.000004503084,0.01709724,0.1349681,0.0009213714,0.004157636,0.04422572,0.002340788],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894243,0.0001077097,0.002842878,0.0004983143,0.00003250556,0.0006703296,0.00002797277,0.00002024807,0.006375766],"genre_scores_gemma":[0.989942,0.00001282491,0.0001084356,0.000008055863,0.00009399262,0.000002552195,0.0004674846,0.00001593569,0.009348783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7736223,"threshold_uncertainty_score":0.5437305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007220449666617098,"score_gpt":0.2313503016312541,"score_spread":0.224129851964637,"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."}}