  Recently, diffusion probabilistic models have attracted attention in
generative time series forecasting due to their remarkable capacity to generate
high-fidelity samples. However, the effective utilization of their strong
modeling ability in the probabilistic time series forecasting task remains an
open question, partially due to the challenge of instability arising from their
stochastic nature. To address this challenge, we introduce a novel
Multi-Granularity Time Series Diffusion (MG-TSD) model, which achieves
state-of-the-art predictive performance by leveraging the inherent granularity
levels within the data as given targets at intermediate diffusion steps to
guide the learning process of diffusion models. The way to construct the
targets is motivated by the observation that the forward process of the
diffusion model, which sequentially corrupts the data distribution to a
standard normal distribution, intuitively aligns with the process of smoothing
fine-grained data into a coarse-grained representation, both of which result in
a gradual loss of fine distribution features. In the study, we derive a novel
multi-granularity guidance diffusion loss function and propose a concise
implementation method to effectively utilize coarse-grained data across various
granularity levels. More importantly, our approach does not rely on additional
external data, making it versatile and applicable across various domains.
Extensive experiments conducted on real-world datasets demonstrate that our
MG-TSD model outperforms existing time series prediction methods.
