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ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction | | Wenxuan Zhang
; Xuechao Zou
; Li Wu
; Jianqiang Huang
; Xiaoying Wang
; | | Date: |
1 Sep 2023 | | Abstract: | 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. | | Source: | arXiv, 2309.00314 | | Services: | Forum | Review | PDF | Favorites | |
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