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Wenzhou-Kean University

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Representative Papers

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret

Jun 01, 2026

This work addresses the instability and lack of robustness in existing single-model weather forecasting approaches across spatiotemporal domains. To overcome these limitations, the authors propose AdaWeather, a novel framework that adaptively combines multiple probabilistic weather forecast models through online learning, dynamically generating a unified probabilistic prediction via a mixture-of-experts strategy. Notably, AdaWeather achieves, for the first time, a logarithmic regret bound relative to the best fixed mixture of experts, outperforming conventional methods that only compete against the single best expert. Experimental results on temperature forecasting demonstrate that AdaWeather significantly surpasses current state-of-the-art approaches, confirming its effectiveness and robustness in real-world forecasting scenarios.

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Latest Papers

AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret

Jun 01, 2026

This work addresses the instability and lack of robustness in existing single-model weather forecasting approaches across spatiotemporal domains. To overcome these limitations, the authors propose AdaWeather, a novel framework that adaptively combines multiple probabilistic weather forecast models through online learning, dynamically generating a unified probabilistic prediction via a mixture-of-experts strategy. Notably, AdaWeather achieves, for the first time, a logarithmic regret bound relative to the best fixed mixture of experts, outperforming conventional methods that only compete against the single best expert. Experimental results on temperature forecasting demonstrate that AdaWeather significantly surpasses current state-of-the-art approaches, confirming its effectiveness and robustness in real-world forecasting scenarios.

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