Abstract
Recent years, weather forecasting has gained significant attention. However, accurately predicting weather remains a challenge due to the rapid variability of meteorological data and potential teleconnections. Current spatiotemporal forecasting models primarily rely on convolution operations or sliding windows for feature extraction. These methods are limited by the size of the convolutional kernel or sliding window, making it difficult to capture and identify potential teleconnection features in meteorological data. Additionally, weather data often involve non-rigid bodies, whose motion processes are accompanied by unpredictable deformations, further complicating the forecasting task. In this paper, we propose the GMG model to address these two core challenges. The Global Focus Module, a key component of our model, enhances the global receptive field, while the Motion Guided Module adapts to the growth or dissipation processes of non-rigid bodies. Through extensive evaluations, our method demonstrates competitive performance across various complex tasks, providing a novel approach to improving the predictive accuracy of complex spatiotemporal data.
Abstract (translated)
近年来，天气预报受到了越来越多的关注。然而，由于气象数据的快速变化和潜在的远程联系（teleconnections），准确预测天气仍然是一个挑战。目前的空间时间预测模型主要依赖于卷积操作或滑动窗口来进行特征提取。这些方法受到卷积核大小或滑动窗口大小的限制，难以捕捉并识别气象数据中的潜在远程联系特征。此外，天气数据通常涉及非刚体物体，其运动过程伴随着不可预知的变形，进一步增加了预测任务的复杂性。 在本文中，我们提出了GMG模型来解决上述两个核心挑战。该模型的关键组成部分是全局关注模块（Global Focus Module），它增强了全局感受野；另一个重要部分是运动引导模块（Motion Guided Module），它可以适应非刚体物体的成长或消散过程。通过广泛的评估，我们的方法在各种复杂任务中展示了竞争性的性能，为提高复杂空间时间数据的预测准确性提供了一种新颖的方法。
URL
https://arxiv.org/abs/2503.11297
