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Long Short Term Memory Networks Based Anomaly Detection for KPIs 被引量:2
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作者 haiqi zhu Fanzhi Meng +4 位作者 Seungmin Rho Mohan Li Jianyu Wang Shaohui Liu Feng Jiang 《Computers, Materials & Continua》 SCIE EI 2019年第8期829-847,共19页
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 an... 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. 展开更多
关键词 LSTM anomaly detection KPIs
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