  Spatiotemporal prediction aims to generate future sequences by paradigms
learned from historical contexts. It is essential in numerous domains, such as
traffic flow prediction and weather forecasting. Recently, research in this
field has been predominantly driven by deep neural networks based on
autoencoder architectures. However, existing methods commonly adopt autoencoder
architectures with identical receptive field sizes. To address this issue, we
propose an Asymmetric Receptive Field Autoencoder (ARFA) model, which
introduces corresponding sizes of receptive field modules tailored to the
distinct functionalities of the encoder and decoder. In the encoder, we present
a large kernel module for global spatiotemporal feature extraction. In the
decoder, we develop a small kernel module for local spatiotemporal information
reconstruction. Experimental results demonstrate that ARFA consistently
achieves state-of-the-art performance on popular datasets. Additionally, we
construct the RainBench, a large-scale radar echo dataset for precipitation
prediction, to address the scarcity of meteorological data in the domain.
