Long Short Term Memory Networks Based Anomaly Detection for KPIs

查看全文 作　　者：Haiqi [1]Zhu;Fanzhi [2]Meng;Seungmin [3]Rho;Mohan [4]Li;Jianyu [1]Wang;Shaohui [1]Liu;Feng [1]Jiang

机构地区：[1]School of Computer Science and Technology,Harbin Institute of Technology,Harbin,150000,China;[2]Institute of Computer Application,China Academy of Engineer Physics,Mianyang,621900,China;[3]Department of Software,Sejong University,Seoul,Korea;[4]Cyberspace Institute of Advanced Technology,Guangzhou University,Guangzhou,510006,China

出　　处：《Computers, Materials & Continua》索引2019年第8期,共19页

摘　　要：In real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their business.However,anomaly detection for these data with various patterns and data quality has been a great challenge,especially without labels.In this paper,we adopt an anomaly detection algorithm based on Long Short-Term Memory(LSTM)Network in terms of reconstructing KPIs and predicting KPIs.They use the reconstruction error and prediction error respectively as the criteria for judging anomalies,and we test our method with real data from a company in the insurance industry and achieved good performance. 关键词：LSTM anomaly detection KPIs

相关文献

参考文献（19）
引证文献（2）
耦合文献（19）

参考文献(19)

引证文献(2)

耦合文献(19)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Long Short Term Memory Networks Based Anomaly Detection for KPIs

参考文献(19)

引证文献(2)

耦合文献(19)

用户提示