自适应融合多个天气预报模型，提升预测准确性和鲁棒性。
AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
- 结合机器学习与专家混合方法，动态调整多模型权重。
- 在温度预测上优于现有方法，实现对最优静态组合的近似。
- 适合需要高可靠性气象预测的场景，如灾害预警。
机器学习推动了概率天气预报模型的发展，使其达到与顶尖数值天气预报相当的水平。然而，没有单一模型能在所有时空条件下持续领先，性能高度依赖具体情境。这促使人们探索自适应融合多种预报的方法以提升效果与鲁棒性。尽管已有研究提出组合预报方案，但多依赖监督学习或预测专家建议的方法。本文提出AdaWeather，一种融合多个概率预报的自适应框架，结合机器学习与专家混合机制，生成统一改进的概率预报。不同于传统专家方法仅针对最优单个专家进行后悔界分析，我们扩展算法与理论分析，证明本方法相对于最优静态专家混合组合具有对数级后悔界。实验聚焦温度预测，结果表明其性能超越现有方法。
原文摘要 · Abstract (English)
Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors. But no model consistently dominates spatio-temporally, and relative performance is highly context-dependent. This motivates adaptive methods for combining multiple forecasts to obtain improvements and robustness. While combined forecasts have been proposed in the literature, these are achieved either through supervised learning or through prediction with expert advice methods. We introduce AdaWeather, an adaptive framework that combines many probabilistic forecasts using both machine learning as well as mixture of experts to arrive at a unified improved probabilistic forecast. While traditional expert methods develop the regret bounds with respect to the best single expert in hindsight, we extend the algorithm and analysis to show our method has logarithmic regret compared to the best static mixture of experts in hindsight. Empirically, we focus on forecasting temperature, and observe improvements over existing methods.
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