{"id":"W2090347573","doi":"10.4018/ijsds.2013100105","title":"A Supervised Classification System of Financial Data Based on Wavelet Packet and Neural Networks","year":2013,"lang":"en","type":"article","venue":"International Journal of Strategic Decision Sciences","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université du Québec à Montréal","funders":"","keywords":"Backpropagation; Support vector machine; Computer science; Artificial neural network; Artificial intelligence; Wavelet; Data mining; Pattern recognition (psychology); Stock market index; Stock market; Machine learning; Data classification","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":[0.00112145,0.0006580445,0.000870365,0.001756006,0.0004314968,0.0009384262,0.0008030169,0.0007618323,0.001548559],"category_scores_gemma":[0.003022521,0.0003075759,0.0005734245,0.001458408,0.0002881242,0.00132006,0.0005648413,0.0006393474,0.0009125886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00041766,"about_ca_system_score_gemma":0.0007623106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002131297,"about_ca_topic_score_gemma":0.001851279,"domain_scores_codex":[0.9992205,0.0001243379,0.00009371385,0.0002075705,0.0002891814,0.00006465987],"domain_scores_gemma":[0.9989443,0.0002715057,0.0001333946,0.0001092532,0.0005085496,0.00003292418],"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":[0.0002621614,0.0003101135,0.004924925,0.0001840807,0.0001147851,0.0001849127,0.0001108188,0.03461478,0.02504377,0.003267806,0.004300791,0.926681],"study_design_scores_gemma":[0.00002561488,0.0001191571,0.003888721,0.00002841377,0.00005197964,0.0001243637,0.00003314619,0.9748863,0.0155904,0.002573951,0.002649448,0.00002836101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03322179,0.0001586973,0.9615322,0.0001097881,0.0001323019,0.0002239583,0.0003028356,0.002899249,0.001419156],"genre_scores_gemma":[0.290689,0.0003170231,0.7032856,0.0001300893,0.0001338288,0.0006338666,0.001082996,0.0001097188,0.003617963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002131297,"threshold_uncertainty_score":0.005930841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3407903503226975,"score_gpt":0.4409922614710494,"score_spread":0.1002019111483519,"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."}}