{"id":"W4391418111","doi":"10.1214/23-aoas1795","title":"Network-level traffic flow prediction: Functional time series vs. functional neural network approach","year":2024,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Time series; Computer science; Series (stratigraphy); Artificial neural network; Traffic flow (computer networking); Artificial intelligence; Machine learning; Geology; Computer network","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":[0.001603169,0.001132102,0.0006795155,0.0009055315,0.0002504366,0.0007784943,0.001457339,0.001011372,0.0009507082],"category_scores_gemma":[0.003875378,0.0002929723,0.0005628341,0.001105065,0.0005762049,0.001977769,0.0006598177,0.001148254,0.0001604903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008808265,"about_ca_system_score_gemma":0.0005998886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01479876,"about_ca_topic_score_gemma":0.007145651,"domain_scores_codex":[0.9995084,0.0001872047,0.00002479968,0.0001478433,0.00008003183,0.00005172539],"domain_scores_gemma":[0.9984592,0.0008998669,0.0001840494,0.0001109547,0.0002789243,0.00006707077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005005248,0.00003874254,0.002670714,0.00003909502,0.00003736765,0.00004170126,0.00002352385,0.9654554,0.0002924288,0.008687883,0.0004404959,0.02222264],"study_design_scores_gemma":[4.580639e-7,0.000003660297,0.0001453534,0.000001487426,0.000002465734,0.000002250433,0.000002424811,0.9986116,0.00002977704,0.001157275,0.00004184596,0.000001419593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09252863,0.001093265,0.9019942,0.0007637327,0.0001177005,0.00003052717,0.000311842,0.0003094165,0.002850702],"genre_scores_gemma":[0.9693117,0.0008376457,0.02769158,0.0001077392,0.0001624056,0.00005185674,0.0004655183,0.00003547483,0.001336194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01479876,"threshold_uncertainty_score":0.02942526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04532785483451583,"score_gpt":0.2309017812429498,"score_spread":0.185573926408434,"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."}}