{"id":"W3046072278","doi":"10.1109/icc40277.2020.9148738","title":"Cellular Traffic Load Prediction with LSTM and Gaussian Process Regression","year":2020,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Benchmark (surveying); Gaussian process; Kriging; Cellular network; Residual; Data mining; Ground-penetrating radar; Regression; Artificial intelligence; Process (computing); Scheme (mathematics); Machine learning; Gaussian; Algorithm; Computer network; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005761769,0.001046164,0.0006630718,0.0005683567,0.0002205609,0.0006364484,0.0009137705,0.0009255423,0.0008640194],"category_scores_gemma":[0.001763112,0.0003154384,0.000471375,0.0008949536,0.000301971,0.0009059078,0.0006675597,0.001347487,0.0005177921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005033193,"about_ca_system_score_gemma":0.0006058025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01173517,"about_ca_topic_score_gemma":0.008029643,"domain_scores_codex":[0.9997579,0.00005063545,0.00001353064,0.00008038873,0.00004597471,0.00005143034],"domain_scores_gemma":[0.9995593,0.0002119961,0.00004607047,0.00004795416,0.0001109501,0.00002369077],"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":[0.0002025083,0.0001226174,0.001567247,0.00004467579,0.00006102994,0.0001009071,0.00004720681,0.8353922,0.005580158,0.001210862,0.002093098,0.1535776],"study_design_scores_gemma":[0.000001125194,0.000005249659,0.0000639226,8.061646e-7,0.000001791304,0.000003051601,0.000001886037,0.9992606,0.0003874919,0.0002265131,0.00004618945,0.000001320763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2004039,0.0009219925,0.7897443,0.0005956951,0.0002214888,0.0000437727,0.000496166,0.0050906,0.002482103],"genre_scores_gemma":[0.9352807,0.0002659949,0.06164784,0.0001223788,0.00008106484,0.00004600888,0.0006285261,0.00008565608,0.001841809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01173517,"threshold_uncertainty_score":0.02333373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006986833573939817,"score_gpt":0.1858139750268878,"score_spread":0.178827141452948,"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."}}