A Gradient Noise-based Dynamic Conditional Diffusion Model for Time-Series Anomaly Detection

Time series anomaly detection is crucial in various real-world scenarios, including fault diagnosis, financial fraud detection, and early warning systems. While diffusion models have recently emerged as powerful generative tools for anomaly detection, two key challenges persist: 1 conventional Gaussian noise used during the forward process fails to suppress anomaly-specific frequencies due to spectral mismatches and 2 most existing methods adopt a unified model to detect all types of anomalies, overlooking the distinct characteristics of trend, seasonal, and mixture anomalies. To address these issues, we propose GNDC-DM, a greadient noise-based dynamic conditional diffusion model for time series anomaly detection. GNDC-DM employs three dedicated channels to detect different types of anomalies individually. In the trend and seasonal channels, we introduce a novel GNDC-DM that fuses gradient-aligned noise with stochastic Gaussian components, effectively preserving normal patterns while corrupting anomaly distortion. In the mixture channel, we dynamically incorporate trend and seasonal components as conditions to guide the denoising process, making mixed anomalies more distinguishable. Extensive experiments on four benchmark datasets demonstrate the superior performance of our approach, highlighting its ability to improve detection accuracy across various anomaly categories.