Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.

翻译：时空预测旨在通过从历史上下文中学习到的范式生成未来序列，其在交通流预测和天气预报等多个领域具有重要意义。然而，现有方法在处理时空相关性时面临挑战，因为它们通常采用编码器和解码器架构具有相同的感受野，这会对预测精度产生不利影响。本文提出了一种非对称感受野自编码器（ARFA）模型以解决该问题。具体而言，我们针对编码器和解码器不同的功能特点，设计了相应大小的感受野模块。在编码器中，我们引入了一个大核模块用于全局时空特征提取；在解码器中，我们开发了一个小核模块用于局部时空信息重建。针对气象预测数据稀缺的问题，我们构建了RainBench——一个针对中国内陆地区独特降水特征的大规模雷达回波数据集，用于降水预测。实验结果表明，ARFA在两个主流时空预测数据集以及我们的RainBench数据集上均取得了持续最优的性能，证实了该方法的有效性。这项工作不仅从感受野的角度探索了一种新方法，还为降水预测提供了数据支持，从而推动未来时空预测研究的进展。