BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Arcitecture for Spatial-Temporal Prediction

The BiDepth Multimodal Neural Network (BDMNN) addresses spatial-temporal prediction challenges by integrating information at variable temporal depths, enhancing accuracy in urban traffic and weather forecasting. This bidirectional depth modulation allows for a comprehensive understanding of long-term trends and short-term fluctuations, outperforming state-of-the-art benchmarks with reduced computational requirements. The BDMNN methodology introduces a novel approach for analyzing spatio-temporal (ST) data, focusing on two key components: BiDepth and TimeSeries Encoders. It addresses existing analytical model gaps by processing data through the BiDepth Encoder, which uses convolutional layers to handle the dual aspects of DeepShallow and ShallowDeep networks. The BiDepth approach allows for efficient adaptation to new information and captures complex, short-term dependencies in ST data, making it suitable for scenarios with varying relationships over time and space. The BDMNN outperforms baseline models in diverse urban contexts, showing over 14.58% improvement in the GPM dataset when added to ConvSelfAttention. This robustness highlights its potential for various ST forecasting applications, enhancing resource planning, risk mitigation, and operational efficiency.