论文标题
改善季节性重度多元时间序列异常检测的鲁棒性
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection
论文作者
论文摘要
时间序列数据的鲁棒异常检测（AD）是监视许多复杂现代系统的关键组件。这些系统通常会产生高维度序列，这些时间序列可能高度嘈杂，季节性和相关性。本文探讨了此类数据中的一些挑战，并提出了一种新的方法，该方法使人们对季节性和受污染数据的鲁棒性提高，同时提供了更好的根部原因识别异常。特别是，我们建议使用健壮的季节性多元生成对抗网络（RSM-GAN），该网络扩展了GAN的最新进展，并采用了卷积LSTM层和注意力机制，以在各种环境中产生出色的性能。我们进行了广泛的实验，其中该模型不仅在复杂的季节性模式上显示出更健壮的行为，而且还显示出对训练数据污染的阻力增加。我们将其与现有的经典和深入学习广告模型进行了比较，并表明该体系结构与最低的假阳性率相关联，并且在现实世界和合成数据中分别将精度提高了30％和16％。
Robust Anomaly Detection (AD) on time series data is a key component for monitoring many complex modern systems. These systems typically generate high-dimensional time series that can be highly noisy, seasonal, and inter-correlated. This paper explores some of the challenges in such data, and proposes a new approach that makes inroads towards increased robustness on seasonal and contaminated data, while providing a better root cause identification of anomalies. In particular, we propose the use of Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN) that extends recent advancements in GAN with the adoption of convolutional-LSTM layers and attention mechanisms to produce excellent performance on various settings. We conduct extensive experiments in which not only do this model displays more robust behavior on complex seasonality patterns, but also shows increased resistance to training data contamination. We compare it with existing classical and deep-learning AD models, and show that this architecture is associated with the lowest false positive rate and improves precision by 30% and 16% in real-world and synthetic data, respectively.
