{"id":"W1983298518","doi":"10.1142/s0219477514500084","title":"IMPROVING FORECASTING ACCURACY OF THE S&P500 INTRA-DAY PRICE DIRECTION USING BOTH WAVELET LOW AND HIGH FREQUENCY COEFFICIENTS","year":2014,"lang":"en","type":"article","venue":"Fluctuation and Noise Letters","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Wavelet; Computer science; Backpropagation; Discrete wavelet transform; Artificial neural network; Low frequency; Wavelet transform; Econometrics; Algorithm; Data mining; Artificial intelligence; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005212469,0.0001780904,0.0002595017,0.0002404805,0.0004044185,0.0001982938,0.000254783,0.00007529488,0.00003695919],"category_scores_gemma":[0.02261081,0.0001237599,0.00006332959,0.0007096037,0.0001898089,0.0003555617,0.0001562676,0.0001741696,0.00000263562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005555738,"about_ca_system_score_gemma":0.00003589061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003047449,"about_ca_topic_score_gemma":0.00002703376,"domain_scores_codex":[0.99711,0.0007991687,0.0006028708,0.0004724129,0.0007570743,0.0002584849],"domain_scores_gemma":[0.9952128,0.003414256,0.0006988473,0.0004089282,0.0001829915,0.00008215567],"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":[0.00003606041,0.00002413693,0.01940374,0.00005497332,0.00001572189,5.386362e-7,0.001789144,0.001061321,0.3269971,0.0001306876,0.0004232466,0.6500634],"study_design_scores_gemma":[0.001138515,0.00005758776,0.4372821,0.000268814,0.00007537739,0.00005151117,0.00008672108,0.5446612,0.01236164,0.002456888,0.001075743,0.0004838361],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6954132,0.00001469262,0.3030832,0.0005254976,0.0005877424,0.0002090633,0.00000389874,0.00002163442,0.0001411225],"genre_scores_gemma":[0.9666072,0.000002565686,0.03231026,0.0008387346,0.0001508736,0.000005404921,0.000002391123,0.00001708458,0.00006545729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6495795,"threshold_uncertainty_score":0.9856222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05891609694618198,"score_gpt":0.320011562993064,"score_spread":0.2610954660468821,"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."}}