{"id":"W3198984013","doi":"10.1109/ijcnn52387.2021.9533423","title":"Versatile Deep Learning Based Application for Time Series Imputation","year":2021,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Missing data; Computer science; Machine learning; Artificial intelligence; Time series; Data modeling; Deep learning; Data mining","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":[0.001689484,0.0008377367,0.0007164956,0.0007709229,0.0002977072,0.0009529728,0.001850914,0.001133571,0.004221232],"category_scores_gemma":[0.005076139,0.0003934216,0.0008456603,0.001301247,0.0002510029,0.001097303,0.001635033,0.00200315,0.00134788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006106212,"about_ca_system_score_gemma":0.00122144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006529097,"about_ca_topic_score_gemma":0.006045149,"domain_scores_codex":[0.9995055,0.000150158,0.00004665798,0.0001068821,0.0001250533,0.00006564625],"domain_scores_gemma":[0.9989128,0.0005016111,0.00008526343,0.000185902,0.000267775,0.00004658329],"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":[0.0002397659,0.0002642609,0.004438723,0.000214693,0.0001725948,0.0002828581,0.00009869361,0.6715151,0.003911757,0.01157882,0.008971799,0.2983109],"study_design_scores_gemma":[0.000004788942,0.00001289606,0.0002040717,0.000009364882,0.00000516955,0.00002126778,0.00000729239,0.9941302,0.001143156,0.00319567,0.001260527,0.000005577423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01266376,0.0003639731,0.9776481,0.0003526358,0.0001001965,0.00005228832,0.0006038516,0.006244827,0.001970302],"genre_scores_gemma":[0.4945334,0.000891586,0.4925625,0.0004571747,0.0001167983,0.0003496029,0.003068991,0.0004949041,0.007525085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006529097,"threshold_uncertainty_score":0.01412141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003115751763169452,"score_gpt":0.1928802373913657,"score_spread":0.1897644856281962,"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."}}