Status prediction and anomaly detection are two fundamental tasks in automatic IT systems monitoring. In this paper, a joint model Predictor & Anomaly Detector (PAD) is proposed to address these two issues under one framework. In our design, the variational auto-encoder (VAE) and long short-term memory (LSTM) are joined together. The prediction block (LSTM) takes clean input from the reconstructed time series by VAE, which makes it robust to the anomalies and noise for prediction task. 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.
翻译:状态预测和异常现象探测是自动信息技术系统监测的两项基本任务。本文件提议在一个框架内建立一个联合模型预测器和异常探测器(PAD),以解决这两个问题。在我们的设计中,将变式自动编码器(VAE)和长期短期内存(LSTM)结合在一起。预测块(LSTM)从VAE重建的时间序列中获取干净的输入,这使它能够对异常和噪音进行稳健的预测任务。与此同时,LSTM块保持长期的连续模式,这是VAE编码窗口所看不到的。这导致VAE在异常探测方面比单独培训的更好表现。在整个处理管道中,光谱残余分析与VAE和LSTM相结合,以提高两者的性能。两项任务的优异性表现与两项具有挑战性的评价基准的实验得到了确认。