论文标题
TFAD：分解时间序列异常检测结构，并进行时频分析
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
论文作者
论文摘要
由于复杂的时间依赖性和有限的标签数据，时间序列异常检测是一个具有挑战性的问题。尽管已经提出了包括传统模型在内的某些算法，但其中大多数主要集中在时间域建模上，并且没有在时间序列数据的频域中充分利用信息。在本文中，我们提出了一个基于时频分析的时间序列异常检测模型，或简称TFAD，以利用时间和频域以提高性能。此外，我们将时间序列的分解和数据增强机制结合在设计的时频体系结构中，以进一步提高性能和解释性的能力。对广泛使用基准数据集的实证研究表明，我们的方法在单变量和多变量时间序列序列检测任务中获得了最先进的性能。代码可在https://github.com/damo-di-ml/cikm22-tfad上提供。
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.
