{"id":"W3184338320","doi":"10.1109/tnsm.2021.3098784","title":"TSAGen: Synthetic Time Series Generation for KPI Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Anomaly detection; Computer science; Data mining; Performance indicator; Time series; Anomaly (physics); Series (stratigraphy); Overhead (engineering); Machine learning","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":[0.001682851,0.001080972,0.0004730188,0.001995182,0.000241064,0.0005978964,0.001117915,0.0004983114,0.002650189],"category_scores_gemma":[0.01194831,0.0002583377,0.0006445779,0.001370819,0.0003232577,0.0007137712,0.0006452746,0.0009222408,0.0008906615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004401793,"about_ca_system_score_gemma":0.0006638077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003585887,"about_ca_topic_score_gemma":0.003163993,"domain_scores_codex":[0.9992056,0.000193499,0.00007727715,0.0001874202,0.0002921063,0.00004403829],"domain_scores_gemma":[0.9960621,0.002262916,0.0002977309,0.0006992233,0.0005522677,0.0001257767],"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":[0.001371834,0.0006520092,0.0300239,0.0006874034,0.0004729093,0.0008275122,0.0005436038,0.4796588,0.02006557,0.01077114,0.06421325,0.390712],"study_design_scores_gemma":[0.00005159705,0.00007839814,0.001790444,0.00000902912,0.00001335513,0.00007576836,0.00002747618,0.9867848,0.005693319,0.002311102,0.003146677,0.00001805578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430307,0.0003044645,0.7279196,0.0004979012,0.0004252768,0.0006645492,0.01481075,0.109296,0.003050794],"genre_scores_gemma":[0.5653643,0.0002068253,0.4039604,0.0001506269,0.00007993934,0.0007837736,0.02592445,0.002272857,0.001256733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003585887,"threshold_uncertainty_score":0.008899868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157556854400971,"score_gpt":0.211022926873319,"score_spread":0.1994473583293093,"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."}}