PSRUNet: a recurrent neural network for spatiotemporal sequence forecasting based on parallel simple recurrent unit

Unsupervised video prediction is widely applied in intelligent decision-making scenarios due to its capability to model unknown scenes. Traditional video prediction models based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) consume large amounts of computational resources while constantly losing the original picture information. This paper addresses the challenges discussed and introduces PSRUNet, a novel model featuring the lightweight ParallelSRU unit. By prioritizing global spatiotemporal features and minimizing redundancy, PSRUNet effectively enhances the model's early perception of complex spatiotemporal changes. The addition of an encoder-decoder architecture captures high-dimensional image information, and information recall is introduced to mitigate gradient vanishing during deep network training. We evaluated the performance of PSRUNet and analyzed the capabilities of ParallelSRU in real-world applications, including short-term precipitation forecasting, traffic flow prediction, and human behavior prediction. Experimental results across multiple video prediction benchmarks demonstrate that PSRUNet achieves remarkably efficient and cost-effective predictions, making it a promising solution for meeting the real-time and accuracy requirements of practical business scenarios.