Clustering-Enhanced Diffusion Model for Time Series Forecasting

Diffusion models have shown significant potential in generating high-quality time series data, especially in high-noise environments. However, the existing diffusion models primarily focused on extracting temporal features. Meanwhile, these models overlook the importance of modeling dynamic cross-channel relationships and sparse correlations in complex multivariate time series, which is insufficient for datasets with strong inter-channel dependencies. In this paper, we propose a clustering-enhanced conditional diffusion model (CE-Diff) that combines the advantages of adaptive channel clustering and conditional diffusion models. It maps time series data to the frequency domain to capture fine-grained correlations between channels. The clustering information is expressed as a sparse channel mask matrix, which dynamically guides the reverse denoising process through a masked attention mechanism. This clustering enhancement strengthens the diffusion model's ability to handle multivariate data. Experiments on several complex multivariate datasets have demonstrated that CE-Diff achieves higher accuracy and better generation quality in time series forecasting (TSF) compared to other generative models.