We study the hypothesis testing problem for unknown dynamical systems. More specifically, we observe sequential input and output data from a dynamical system with unknown parameters, and we aim to determine whether the collected data is from a null distribution. Such a problem can have many applications. Here we formulate anomaly detection as hypothesis testing where the anomaly is defined through the alternative hypothesis. Consequently, hypothesis testing algorithms can detect faults in real-world systems such as robots, weather, energy systems, and stock markets. Although recent works achieved state-of-the-art performances in these tasks with deep learning models, we show that a careful analysis using hypothesis testing and graphical models can not only justify the effectiveness of autoencoder models, but also lead to a novel neural network design, termed DyAD (DYnamical system Anomaly Detection), with improved performances. We then show that DyAD achieves state-of-the-art performance on several existing datasets and a new dataset on battery anomaly detection in electric vehicles.

翻译：我们研究未知动态系统的假设检验问题。具体而言，我们观测具有未知参数的动态系统产生的连续输入与输出数据，旨在判断所收集数据是否来自零假设分布。此类问题具有广泛应用场景。本文将异常检测构建为假设检验问题，其中异常通过备择假设定义。因此，假设检验算法能够检测现实系统（如机器人、天气、能源系统和股票市场）中的故障。尽管近期研究利用深度学习模型在这些任务上取得了最先进性能，但本文表明，通过结合假设检验与图模型的细致分析，不仅能证明自编码器模型的有效性，还可催生一种名为DyAD（动态系统异常检测）的新型神经网络设计，从而提升性能。我们进一步证明，在多个现有数据集以及电动汽车电池异常检测新数据集上，DyAD均实现了最先进的性能。