mlsanityData to 7 Oct 2026

Masked Autoregressive Model for Weather Forecasting

arXiv 2409.20117 ↗First posted 30 Sep 2024cs.CVcited by 1

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Abstract (arXiv)

The growing impact of global climate change amplifies the need for accurate and reliable weather forecasting. Traditional autoregressive approaches, while effective for temporal modeling, suffer from error accumulation in long-term prediction tasks. The lead time embedding method has been suggested to address this issue, but it struggles to maintain crucial correlations in atmospheric events. To overcome these challenges, we propose the Masked Autoregressive Model for Weather Forecasting (MAM4WF). This model leverages masked modeling, where portions of the input data are masked during training, allowing the model to learn robust spatiotemporal relationships by reconstructing the missing information. MAM4WF combines the advantages of both autoregressive and lead time embedding methods, offering flexibility in lead time modeling while iteratively integrating predictions. We evaluate MAM4WF across weather, climate forecasting, and video frame prediction datasets, demonstrating superior performance on five test datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 39 of its 54 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

39 of 54 references.

References · 39

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27 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEscited by 1