Time Series Anomaly Detection Methods Incorporating Wavelet Decomposition and Temporal Decoupled Autoencoder

Time series anomaly detection is one of the critical tasks in time series analysis. There are many existing works using autoencoders for time series anomaly detection; however, autoencoders can not only reconstruct normal sequences but also reconstruct anomalous sequences sometimes, which leads to the poor effect of anomaly detection based on reconstruction error. At the same time, the real-world time series have the characteristic of temporal coupling, and the existing methods often cannot take into account the multiple modes of the time series, which leads to the low accuracy of time series anomaly detection. Aiming at the above problems, we propose a Time Series Anomaly Detection method incorporating Wavelet Decomposition and Temporal Decoupled Autoencoder (WDAE). Specifically, WDAE first reshapes the original 1D time series into 2D data harboring different temporal patterns based on frequency components and independently extracts the different temporal features of the time series data. Then, the original data are reconstructed in the time domain by the autoencoder to obtain the reconstructed time-domain sequence, and in the frequency domain, the 2D data are subjected to the discrete wavelet transform and the inverse wavelet transform after weight adjustment to obtain the reconstructed frequency domain sequence, and the captured normal sequence pattern in the frequency domain restricts the latent feature space in the time domain so that the autoencoder can rebuild the normal sequence to avoid reconstructing the anomalous sequence well. Experiments on five publicly available data show that WDAE significantly improves the F1 value over related state-of-the-art baseline methods.