Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation
For practitioners needing coordinated forecasts across many systems (e.g., economics, healthcare), ESE offers a fast, scalable, and robust alternative to sequential prediction.
ESE introduces a new paradigm for simultaneous forecasting of multiple interacting systems, achieving accuracy comparable to SOTA methods while delivering a 10-70x speedup and linear-time complexity.
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.