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

2026-06-11

AI Summary

This work addresses the inefficiency of conventional system-by-system approaches in jointly forecasting multiple interdependent systems by introducing the Equilibrium State Estimation (ESE) paradigm. ESE enables synchronous estimation of equilibrium states for all systems through a single forward inference pass and generates holistic predictions based on deviations between current and equilibrium states. The method achieves, for the first time, efficient synchronized prediction with linear time complexity, delivering 10–70× speedup over existing approaches while maintaining or even surpassing their accuracy. Moreover, ESE demonstrates strong robustness and generalization as system scale increases. It attains state-of-the-art performance on real-world datasets, including those involving exchange rates and epidemic spread.

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 settings 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 10-70x 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.

Problem

• simultaneous forecasting
• scalability
• multi-system prediction
• equilibrium state
• computational efficiency

Innovation

• Equilibrium State Estimation
• Simultaneous Forecasting
• Scalable Prediction
• Multi-system Modeling
• Linear-time Complexity