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

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Equilibrium State Estimation (ESE) is introduced as a novel paradigm for simultaneously forecasting multiple interacting systems, such as currency exchange rates or epidemic spread. Unlike traditional methods that predict systems individually, ESE processes all systems in a single pass. It operates by first estimating a collective equilibrium state across systems, then generating holistic forecasts based on the deviation between the current state and this estimated equilibrium. Experiments on synthetic and real-world datasets, including 16 G20 currency exchange rates and COVID-19 spread across 20, 79, and 320 regions in Victoria, Australia, demonstrate ESE's accuracy is comparable to state-of-the-art (SOTA) methods. Crucially, ESE achieves significant speedups, ranging from 10-70x, and exhibits linear-time complexity, ensuring superior scalability as the number of systems increases. It also integrates seamlessly with existing predictors and maintains accuracy under various perturbations.

Key takeaway

For Machine Learning Engineers developing forecasting solutions for multiple interacting systems, such as economic indicators or disease spread, you should evaluate Equilibrium State Estimation (ESE). This method provides comparable accuracy to state-of-the-art models while delivering substantial speedups (10-70x) and superior scalability. Integrating ESE can significantly reduce computational costs and enhance the efficiency of your multi-system prediction pipelines, especially for large-scale or high-granularity applications.

Key insights

ESE simultaneously forecasts interacting systems by estimating their collective equilibrium state, offering speed and scalability.

Principles

• Interacting systems can be jointly predicted via a collective equilibrium state.
• Equilibrium state estimation can significantly reduce computational cost.
• Attribute data is crucial for estimating system-level equilibrium dynamics.

Method

ESE estimates a statistical equilibrium state from system attributes, then forecasts future states based on the current state's deviation from this estimated equilibrium, using a predictor for overall trend.

In practice

• Integrate ESE with existing predictors for 10-70x speedup.
• Apply ESE to large-scale multi-system forecasting tasks.
• Use attribute data to model inter-system relationships.

Topics

• Equilibrium State Estimation
• Simultaneous Forecasting
• Multi-system Prediction
• Time Series Forecasting
• Computational Efficiency
• Scalable Machine Learning

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.