AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
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Computer Science > Machine Learning
arXiv:2606.02663 (cs)
[Submitted on 1 Jun 2026]
Title:AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
Authors:Saptarishi Dhanuka (Ashoka University), Sarvesh Iyer (Ashoka University), Manmeet Singh (Western Kentucky University), Mihir More (Ashoka University), Rushil Gupta (Ashoka University), Dhruman Gupta (Ashoka University), Parthasarathi Mukhopadhyay (Ashoka University), Sandeep Juneja (Ashoka University)
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Abstract: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.
| Comments: | 36 pages, 16 figures. Submitted to arXiv. Forecast aggregation for probabilistic weather prediction using offline supervised learning and online prediction with expert advice. Includes theoretical regret guarantees and empirical evaluation on temperature forecasting. Submitted to NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| MSC classes: | 68Q32 (Primary), 68W27, 62P12, 62M20 (Secondary) |
| ACM classes: | I.2.6; I.2.8; G.3 |
| Cite as: | arXiv:2606.02663 [cs.LG] |
| (or arXiv:2606.02663v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.02663
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Saptarishi Dhanuka [view email][v1] Mon, 1 Jun 2026 09:46:26 UTC (7,167 KB)
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