Recent innovations in diffusion probabilistic models have paved the way for
significant progress in image, text and audio generation, leading to their
applications in generative time series forecasting. However, leveraging such
abilities to model highly stochastic time series data remains a challenge. In
this paper, we propose a novel Stochastic Diffusion (StochDiff) model which
learns data-driven prior knowledge at each time step by utilizing the
representational power of the stochastic latent spaces to model the variability
of the multivariate time series data. The learnt prior knowledge helps the
model to capture complex temporal dynamics and the inherent uncertainty of the
data. This improves its ability to model highly stochastic time series data.
Through extensive experiments on real-world datasets, we demonstrate the
effectiveness of our proposed model on stochastic time series forecasting.
Additionally, we showcase an application of our model for real-world surgical
guidance, highlighting its potential to benefit the medical community.

通过利用扩散概率模型和随机扩散（StochDiff）模型，本研究提出了一种新型方法，学习每个时间步长的数据先验知识，从而更好地对高度随机化的时间序列进行建模，通过实验验证了该方法在随机时间序列预测中的有效性，并展示了在实际医疗指导中的应用潜力。