一次计算同时预测多个相互关联系统，速度提升10-70倍。
Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation
- 通过估计系统间平衡状态，统一生成多系统预测
- 在汇率和疫情数据上精度达顶尖水平，速度提升10-70倍
- 适合大规模多系统预测场景，对扰动鲁棒性强
我们提出平衡状态估计（ESE），一种新型的同步预测范式，用于需独立但协同预测的多个交互系统。与传统逐个预测的方法不同，ESE在单次前向传播中完成所有系统预测。其先估计系统间的平衡状态，再基于当前状态与平衡状态的差异生成全局预测。在合成数据及真实世界数据集（包括货币汇率、新冠疫情传播建模）上的大量实验表明，ESE精度不低于现有最先进方法，且显著更快。ESE可无缝集成于传统预测器，结合其准确性与自身高效性，实现10-70倍加速。具备线性时间复杂度，随系统数量增加仍能良好扩展。此外，在多种扰动下仍保持高精度，证明ESE是快速、通用、鲁棒且可扩展的多预测方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。