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.

翻译：近年来，天气预报受到了广泛关注。然而，由于气象数据的快速多变性和潜在的遥相关现象，准确预测天气仍是一项挑战。当前的时空预测模型主要依赖卷积运算或滑动窗口进行特征提取。这些方法受限于卷积核或滑动窗口的大小，难以捕捉和识别气象数据中潜在的遥相关特征。此外，天气数据常涉及非刚体，其运动过程伴随着不可预测的形变，这进一步增加了预测任务的复杂性。本文提出GMG模型以应对这两个核心挑战。模型的关键组成部分——全局聚焦模块，增强了全局感受野；而运动引导模块则适应非刚体的增长或消散过程。通过大量评估，我们的方法在各种复杂任务中展现出具有竞争力的性能，为提高复杂时空数据的预测准确性提供了一种新途径。