Unsupervised diffusion based anomaly detection for time series

Unsupervised anomaly detection aims to construct a model that effectively detects invisible anomalies by training and reconstruct normal data. While a significant amount of reconstruction-based methods has made effective progress for time series anomaly detection, challenges still exist in aspects such as temporal feature extraction and generalization ability. Firstly, temporal features of data are subject to local information interference in reconstruction methods, which limits the long-term signal reconstruction methods. Secondly, the training dataset collector is subject to information nourishment such as collection methods, collection periods and locations, and data patterns are diverse, requiring the model to rebuild normal data according to different patterns. These issues hinder the anomaly detection capability of reconstruction-based methods. We propose an unsupervised anomaly detection model based on a diffusion model, which learns normal data pattern learning through noisy forward diffusion and reverse noise regression. By using a cascaded structure and combining it with a structured state space layer, long-term time series signal feature can be well extracted. Different collection signals are distinguished by introducing collector entity ID embedding. The method proposed in this article significantly improves performance in experimental tests on three public datasets. Innovative aspects: (1) Utilizing the S4 method to capture long-term dependencies; (2) Employing a diffusion model for reconstruction learning; (3) Leveraging embedding techniques to enhance different pattern learning.