Bi-PredRNN: An Enhanced PredRNN++ with a Bidirectional Network for Spatiotemporal Sequence Prediction - Han, Seung Hyun; Cho, Da Jung; Chung, Tae Sun (Electronics (Switzerland), 2024)
In recent years, significant advancements have been made in spatiotemporal sequence prediction, with PredRNN++ emerging as a powerful model due to its superior ability to capture complex temporal dependencies. However, the current unidirectional nature of PredRNN++ limits its ability to fully exploit the temporal information inherent in many real-world sequences. In this research, we propose an enhancement to the PredRNN++ model by incorporating a bidirectional network, enabling the model to consider both past and future contexts during prediction. This bidirectional extension enhances the model’s ability to predict sequences accurately and reliably, especially for data with intricate temporal patterns. Our experimental results demonstrate that the Bidirectional PredRNN++ outperforms the original model across several benchmark datasets, highlighting its potential for a wide range of applications in spatiotemporal data analysis.
Keywords: extreme-scale data prediction; predictive learning; spatiotemporal data