Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation

Beinan Xu, Andy Song, Jiti Gao, Feng Liu

Keywords: prediction, equilibrium, exchange, COVID-19

TL;DR: A new method for predicting multiple systems in a single run at high speed.

Abstract: We introduce Equilibrium State Estimation (ESE), a novel paradigm for simultaneous prediction, where multiple interacting systems require separate yet coordinated forecasts. Such scenarios often arise in real-world such as economics and healthcare modeling. Unlike existing approaches that predict one system at a time, ESE forecasts all systems in a single pass. It first estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. Extensive experiments on synthetic and real-world datasets, including currency exchange and COVID-19 spread modeling, demonstrate that ESE is at least as accurate as state-of-the-art (SOTA) methods while being significantly faster. In addition, ESE integrates seamlessly with conventional predictors, combining their accuracy with its exceptional efficiency and delivering a 10–70× speedup. With linear-time complexity, ESE scales far better than SOTA methods as the number of systems increases. Moreover, it remains accurate under diverse perturbations, establishing ESE as a fast, generalizable, robust, and scalable multi-prediction method. Source code and data are available at https://anonymous.4open.science/r/ESE-C339.

Primary Area: learning on time series and dynamical systems

Submission Number: 11874