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

Problem
The paper addresses the challenge of simultaneous forecasting in multi-system environments, where traditional methods typically predict one system at a time. This limitation can lead to inefficiencies and inaccuracies in scenarios involving interdependent systems, such as economic models or healthcare predictions. The authors propose a novel approach, Equilibrium State Estimation (ESE), to overcome these limitations. This work is a preprint and has not yet undergone peer review.

Method
ESE operates by first estimating an equilibrium state across multiple interacting systems, which serves as a reference point for generating forecasts. The method calculates the difference between the current state and the estimated equilibrium to produce holistic predictions for all systems simultaneously. The architecture is designed to achieve linear-time complexity, allowing it to scale efficiently as the number of systems increases. The authors conducted extensive experiments on both synthetic datasets and real-world applications, including currency exchange rates and COVID-19 spread modeling. ESE can be integrated with existing predictors, enhancing their accuracy while significantly improving computational efficiency.

Results
ESE demonstrates competitive performance against state-of-the-art (SOTA) forecasting methods, achieving comparable accuracy while providing a substantial speed advantage. The reported speedup ranges from 10x to 70x compared to traditional methods, depending on the complexity and number of systems involved. The experiments indicate that ESE maintains its accuracy under various perturbations, showcasing robustness in its predictions. Specific benchmark comparisons were made, although exact numerical results against named baselines were not detailed in the abstract.

Limitations
The authors acknowledge that while ESE is effective, it may still be sensitive to the quality of the equilibrium state estimation, which could impact the overall forecasting accuracy. Additionally, the paper does not address potential limitations related to the scalability of the equilibrium estimation process itself, particularly in highly dynamic systems where equilibrium states may shift rapidly. The generalizability of ESE to all types of multi-system interactions remains to be fully explored.

Why it matters
The introduction of ESE has significant implications for fields requiring simultaneous forecasting of interdependent systems, such as economics and public health. Its ability to provide fast, accurate predictions can enhance decision-making processes in these domains. The integration capability with existing forecasting models allows practitioners to leverage ESE’s efficiency without sacrificing accuracy. This work contributes to the growing body of literature on scalable forecasting methods, as published in arXiv cs.AI, and sets the stage for future research into more complex multi-system interactions and their forecasting challenges.