A Joint Model for Anomaly Detection and Trend Prediction on IT Operation Series

Anomaly detection and trend prediction are two fundamental tasks in automatic IT systems monitoring. In this paper, a joint model Anomaly Detector & Trend Predictor (ADTP) is proposed. In our design, the variational auto-encoder (VAE) and long short-term memory (LSTM) are joined together to address both anomaly detection and trend prediction. The prediction block (LSTM) takes clean input from the reconstructed time series by VAE, which makes it robust to the anomalies and noise. In the meantime, the LSTM block maintains the long-term sequential patterns, which are out of the sight of a VAE encoding window. This leads to the better performance of VAE in anomaly detection than it is trained alone. In the whole processing pipeline, the spectral residual analysis is integrated with VAE and LSTM to boost the performance of both. The superior performance on two tasks is confirmed with the experiments on two challenging evaluation benchmarks.