Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems

TL;DR

This paper introduces CoST, a novel framework combining deterministic and diffusion models to improve probabilistic spatiotemporal forecasting accuracy and efficiency across diverse real-world systems.

Contribution

CoST is a new collaborative framework that integrates deterministic and diffusion models with a residual decomposition strategy for better spatiotemporal forecasting.

Findings

Achieves 25% performance improvement over state-of-the-art methods

Reduces computational costs significantly

Demonstrates effectiveness across ten real-world datasets

Abstract

Probabilistic forecasting is crucial for real-world spatiotemporal systems, such as climate, energy, and urban environments, where quantifying uncertainty is essential for informed, risk-aware decision-making. While diffusion models have shown promise in capturing complex data distributions, their application to spatiotemporal forecasting remains limited due to complex spatiotemporal dynamics and high computational demands. we propose CoST, a general forecasting framework that collaborates deterministic and diffusion models for diverse spatiotemporal systems. CoST formulates a mean-residual decomposition strategy: it leverages a powerful deterministic model to capture the conditional mean and a lightweight diffusion model to learn residual uncertainties. This collaborative formulation simplifies learning objectives, improves accuracy and efficiency, and generalizes across diverse spatiotemporal systems. To address spatial heterogeneity, we further design a scale-aware diffusion mechanism to guide the diffusion process. Extensive experiments across ten real-world datasets from climate, energy, communication, and urban systems show that CoST achieves 25\% performance gains over state-of-the-art baselines, while significantly reducing computational cost.