Long Short Term Memory Networks Based Anomaly Detection for KPIs

• 作者: Fanzhi Meng Feng Jiang Haiqi Zhu Jianyu Wang Mohan Li Seungmin Rho Shaohui Liu
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: LSTM ANOMALY DETECTION KPIs
• 年，卷（期）: 2019,（8）
• 页码范围: 829-847
• 页数: 19页
• 分类号: TP3

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.