{"id":"W4389636146","doi":"10.1016/j.engappai.2023.107649","title":"Microservices performance forecast using dynamic Multiple Predictor Systems","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco","keywords":"Computer science; Autoregressive integrated moving average; Robustness (evolution); Pooling; Random forest; Performance prediction; Support vector machine; Data mining; Boosting (machine learning); Time series; Machine learning; Artificial intelligence; Simulation","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":[0.0006631077,0.0005838177,0.0008043078,0.0009377156,0.0003459488,0.0008693417,0.000565982,0.0004895133,0.00140893],"category_scores_gemma":[0.002220049,0.0002896244,0.0002580876,0.0007707679,0.0001786423,0.001053717,0.0004794325,0.0009762904,0.0004588302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006161351,"about_ca_system_score_gemma":0.0006786175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0108064,"about_ca_topic_score_gemma":0.01091509,"domain_scores_codex":[0.9996094,0.00006119994,0.00002058583,0.0001237019,0.0001311095,0.00005401026],"domain_scores_gemma":[0.9989838,0.0004135415,0.0001093588,0.0001105846,0.0003225275,0.00006023467],"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":[0.0003218807,0.0001756702,0.01094609,0.00003130524,0.00005636041,0.00005023873,0.00003867828,0.8889361,0.004559559,0.001412939,0.001788394,0.09168279],"study_design_scores_gemma":[0.000001843832,0.0000128553,0.0006931593,0.000001116778,0.000003136129,0.000002494029,0.000003173533,0.9984243,0.0005637541,0.0002298269,0.00006165683,0.000002698608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.459955,0.0002616146,0.5308551,0.0003897887,0.0003087631,0.00005216698,0.0005756623,0.003882126,0.003719729],"genre_scores_gemma":[0.9833111,0.00006349392,0.01511669,0.00001755091,0.00003271053,0.00001924167,0.0002847419,0.00003548169,0.001119045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0108064,"threshold_uncertainty_score":0.021487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02066341503822661,"score_gpt":0.243905932089341,"score_spread":0.2232425170511144,"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."}}