Multivariate Time Series Anomaly Detection using DiffGAN Model
This work improves anomaly detection for multivariate time series, which is critical for applications like industrial monitoring and cybersecurity, but it is incremental as it builds on existing diffusion models with a GAN enhancement.
The paper tackles the problem of multivariate time series anomaly detection by addressing the impact of diffusion steps on reconstruction accuracy, proposing DiffGAN which integrates a GAN into the denoiser of a diffusion model to simultaneously generate noisy data and predict diffusion steps, resulting in superior performance compared to state-of-the-art models.
In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection. The partial diffusion strategy, which depends on the diffusion steps, is commonly used for anomaly detection in these models. However, different diffusion steps have an impact on the reconstruction of the original data, thereby impacting the effectiveness of anomaly detection. To address this issue, we propose a novel method named DiffGAN, which adds a generative adversarial network component to the denoiser of diffusion model. This addition allows for the simultaneous generation of noisy data and prediction of diffusion steps. Compared to multiple state-of-the-art reconstruction models, experimental results demonstrate that DiffGAN achieves superior performance in anomaly detection.