Double Branch Model Based on Discrete Wavelet Transform for Spatiotemporal Prediction

In recent years, spatiotemporal sequence prediction has received increasing attention from researchers and has a wide range of promising applications in the fields of meteorology, traffic flow prediction, and autonomous driving. However, existing spatiotemporal sequence prediction models have some problems, such as slow convergence, training difficulties, and loss of image structural and detail information. We propose a novel end-to-end two-branch spatiotemporal sequence prediction model, which has been improved on these issues. We have compared our model with current advanced models using two datasets and found that our model reached or exceeded the level of the other advanced models in several metrics.