{"id":"W4379231583","doi":"10.23977/cpcs.2023.070108","title":"Cell base station traffic prediction based on GRU","year":2023,"lang":"en","type":"article","venue":"Computing Performance and Communication systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Base station; Artificial neural network; Computer science; Data mining; Traffic generation model; Convolutional neural network; Base (topology); Network traffic simulation; Time series; Real-time computing; Artificial intelligence; Simulation; Network traffic control; Computer network; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002632278,0.0007205245,0.0006631495,0.0009019717,0.000323221,0.0006476935,0.0007483228,0.000410705,0.0008507594],"category_scores_gemma":[0.001232717,0.0002153131,0.0004039437,0.001016281,0.0002148953,0.000844753,0.0003400878,0.0005512757,0.0004127039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006423857,"about_ca_system_score_gemma":0.0005572793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02579965,"about_ca_topic_score_gemma":0.0152097,"domain_scores_codex":[0.9997131,0.00003815591,0.00001055911,0.00008119892,0.0001133182,0.00004361312],"domain_scores_gemma":[0.9997078,0.00006638423,0.00003171075,0.00003410975,0.0001423491,0.00001768894],"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":[0.00009218612,0.00007239865,0.007817768,0.00003450007,0.00004309369,0.00009442357,0.00004132069,0.9051983,0.004854813,0.001143903,0.001449367,0.07915795],"study_design_scores_gemma":[9.142757e-7,0.000006070034,0.0004327426,8.784608e-7,0.000002810453,0.000004885319,0.000003315672,0.9988855,0.0004625558,0.0001254198,0.000072542,0.000002499492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5164428,0.0006018557,0.4695113,0.0002877952,0.000187557,0.00006727164,0.0006017636,0.003368055,0.008931661],"genre_scores_gemma":[0.9867561,0.0001646289,0.01143208,0.00001970882,0.00001479205,0.00002445007,0.0003044383,0.00003532015,0.001248538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02579965,"threshold_uncertainty_score":0.05129898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469243258691223,"score_gpt":0.2113310743769374,"score_spread":0.1966386417900251,"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."}}