论文标题
具有时间序列异常检测的层次潜在因素的深层生成模型
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
论文作者
论文摘要
近年来，多元时间序列序列异常检测已成为一个积极的研究领域，深度学习模型在基准数据集上的表现优于先前的方法。在基于重建的模型中，大多数以前的工作都集中在变化自动编码器和生成对抗网络上。这项工作介绍了DGHL，这是一个新的生成模型家族，用于时间序列异常检测，通过通过后验采样和交替的背部传播来最大程度地提高观察到的可能性。自上而下的卷积网络将新颖的分层潜在空间映射到时间序列窗口，从而利用时间动力来有效地编码信息。尽管依靠后验采样，但它在计算上比目前的方法更有效，比基于RNN的模型的训练时间最高10倍。我们的方法在四个流行的基准数据集上优于当前最新模型。最后，DGHL对于实体之间的可变特征和准确的变量也很强，即使有大量缺失值，设置与物联网的出现相关性。我们通过本文中的新颖遮挡实验证明了DGHL的出色鲁棒性。我们的代码可在https://github.com/cchallu/dghl上找到。
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets. Among reconstruction-based models, most previous work has focused on Variational Autoencoders and Generative Adversarial Networks. This work presents DGHL, a new family of generative models for time series anomaly detection, trained by maximizing the observed likelihood by posterior sampling and alternating back-propagation. A top-down Convolution Network maps a novel hierarchical latent space to time series windows, exploiting temporal dynamics to encode information efficiently. Despite relying on posterior sampling, it is computationally more efficient than current approaches, with up to 10x shorter training times than RNN based models. Our method outperformed current state-of-the-art models on four popular benchmark datasets. Finally, DGHL is robust to variable features between entities and accurate even with large proportions of missing values, settings with increasing relevance with the advent of IoT. We demonstrate the superior robustness of DGHL with novel occlusion experiments in this literature. Our code is available at https://github.com/cchallu/dghl.
