Anomaly Detection for Telemetry Time Series Using a Denoising Diffusion Probabilistic Model

Efficient anomaly detection in telemetry time series is of great importance to ensure the safety and reliability of spacecraft. However, traditional methods are complicated to train, have a limited ability to maintain details, and do not consider temporal-spatial patterns. These problems make it still a challenge to effectively identify anomalies for multivariate time series. In this article, we propose Denoising Diffusion Time Series Anomaly Detection (DDTAD), an unsupervised reconstruction-based method using a denoising diffusion probabilistic model (DDPM). Our model offers the advantages of training stability, flexibility, and robust high-quality sample generation. We employ 1-D-U-Net architecture to capture both temporal dependencies and intervariable information. We restore the anomalous regions from the noise-corrupted input while preserving the precise features of the normal regions intact. Anomalies are identified as discrepancies between the original time series input and its corresponding reconstruction. Experiments on two public datasets demonstrate that our method outperforms the current dominant data-driven methods and enables the accurate detection of point anomalies, contextual anomalies, and subsequence anomalies.