TWavefussion: Wavelet-based Diffusion with Transformer for Multivariate Time Series Anomaly Detection

Hongjun Sheng, Xinggan Peng, Van Kwan Zhi Koh, Bihan Wen, Zhiping Lin

Abstract: Multivariate Time Series (MTS) anomaly detection is challenging in distinguishing anomalous data from normal data in high-dimensional, complex distributions. Even some deep learning methods still have difficulties capturing intricate MTS patterns. Recent advancements in Diffusion Models (DM) for sample generation have inspired us to explore their potential in MTS anomaly detection. In this paper, TWavefussion, an unsupervised diffusion model for MTS anomaly detection combining wavelet-based diffusion model and transformer autoencoder, is proposed. The wavelet-based diffusion model captures fine-grained local features in the high-frequency components of latent features and helps fuse both local and global MTS features better. Comparative experiments show TWavefussion achieves leading performance on three of four datasets.

External IDs:dblp:conf/iscas/ShengPKW025