{"id":"W1998452184","doi":"10.1007/s00521-012-0977-3","title":"Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning","year":2012,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoregressive integrated moving average; Computer science; Nonparametric statistics; Parametric statistics; Artificial intelligence; Artificial neural network; Fuzzy logic; Nonlinear system; Traffic flow (computer networking); Chaotic; Machine learning; Robustness (evolution); Moving average; Time series; Mathematics; Econometrics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001258012,0.0001425762,0.0001280816,0.0002009162,0.0002443159,0.00006349987,0.00007615145,0.00005713098,0.000001146009],"category_scores_gemma":[0.00001107542,0.0001423051,0.00002866178,0.0004325618,0.00004875142,0.00008657471,0.00005462097,0.0002387786,0.000001909272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001662913,"about_ca_system_score_gemma":0.000001735084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000104123,"about_ca_topic_score_gemma":6.15108e-7,"domain_scores_codex":[0.99928,0.00002090593,0.0001768545,0.0001842929,0.0001009204,0.000237033],"domain_scores_gemma":[0.9996421,0.0001066175,0.0000286688,0.0000955646,0.00001236755,0.0001146821],"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":[0.000001004745,0.00004198032,0.02765659,0.00005507364,0.00001368188,2.458659e-7,0.00008700385,0.03642146,0.00004235872,0.0002601744,0.00007313947,0.9353473],"study_design_scores_gemma":[0.0000729216,0.00001742255,0.1166279,0.000009417661,0.00001595782,0.00003131732,0.00002809865,0.8825945,0.00001792466,0.00001403603,0.0004404783,0.0001300486],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.640417,0.0003319413,0.3572037,0.00001818144,0.00004704614,0.0002120448,0.00000154506,0.0014965,0.000272068],"genre_scores_gemma":[0.9936911,0.00004203544,0.005987894,0.00001128252,0.0001555736,0.00004854914,0.00003119078,0.00001787262,0.00001448869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9352173,"threshold_uncertainty_score":0.5803034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04713314749541403,"score_gpt":0.2399615510425517,"score_spread":0.1928284035471377,"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."}}