STPNet: a recurrent neural network for spatiotemporal processes predictive learning

Spatiotemporal process prediction can assist in future spatiotemporal planning and decision-making. However, existing deep learning models still have improvement potential in expressing the complex relationships between spatially interrelated features in time series for spatiotemporal processes. In this paper, a spatiotemporal process prediction model (STPNet) is proposed and it is constructed by a novel stacked module, namely spatiotemporal attention unit (STA-LSTM unit). Compare with units in other spatiotemporal prediction models, the STA-LSTM unit combines the gating mechanism with the attention strategy to present the relationships of historical information and the dependency relationship between spatial features. Comprehensive experiments were conducted on three representative datasets, MovingMNIST, KTH Action, and Radar Echo, and results indicate that STPNet is highly applicable and effective in learning long-term dynamic features, making it suitable for a wide range of sequence-to-sequence spatiotemporal process prediction tasks.