Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting

TL;DR

This paper introduces StochDiff, a novel diffusion probabilistic model that effectively captures the uncertainty and complex dynamics of highly stochastic multivariate time series data, improving forecasting accuracy.

Contribution

The paper presents a new stochastic diffusion model that learns data-driven priors at each time step using stochastic latent spaces, enhancing stochastic time series modeling.

Findings

Outperforms existing models on real-world datasets

Effectively captures uncertainty in stochastic time series

Demonstrates potential in medical applications like surgical guidance

Abstract

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.