Anomaly detection for multivariate time series is an essential task in the modern industrial field. Although several methods have been developed for anomaly detection, they usually fail to effectively exploit the metrical-temporal correlation and the other dependencies among multiple variables. To address this problem, we propose a stacked attention autoencoder for anomaly detection in multivariate time series (SA2E-AD); it focuses on fully utilizing the metrical and temporal relationships among multivariate time series. We design a multiattention block, alternately containing the temporal attention and metrical attention components in a hierarchical structure to better reconstruct normal time series, which is helpful in distinguishing the anomalies from the normal time series. Meanwhile, a two-stage training strategy is designed to further separate the anomalies from the normal data. Experiments on three publicly available datasets show that SA2E-AD outperforms the advanced baseline methods in detection performance and demonstrate the effectiveness of each part of the process in our method.

多元时间序列的异常检测是现代工业领域的一项重要任务。尽管已经开发了几种用于异常检测的方法，但它们通常无法有效地利用度量时间相关性和多个变量之间的其他依赖性。为了解决这个问题，我们提出了一种用于多元时间序列异常检测的堆叠注意力自动编码器（SA2E-AD）；它侧重于充分利用多元时间序列之间的度量和时间关系。我们设计了一个多注意力块，在层次结构中交替包含时间注意力和度量注意力组件，以更好地重建正常时间序列，这有助于区分异常与正常时间序列。同时，设计了两阶段训练策略，以进一步将异常数据与正常数据分开。在三个公开数据集上的实验表明，SA2E-AD 在检测性能方面优于先进的基线方法，并证明了我们方法中过程每个部分的有效性。