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

• URL: http://arxiv.org/abs/2501.08411v2
• Date: Thu, 06 Feb 2025 04:35:56 GMT
• Title: BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Architecture for Spatial-Temporal Prediction
• Authors: Sina Ehsani, Fenglian Pan, Qingpei Hu, Jian Liu,
• Abstract summary: This paper proposes the BiDepth Multimodal Neural Network (BDMNN) with bidirectional depth modulation.<n>Case studies show significant improvements in prediction accuracy, with a 12% reduction in Mean Squared Error for urban traffic prediction and a 15% improvement in rain precipitation forecasting.
• Score: 4.263291797886899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of spatial-temporal (ST) information in dynamic systems, such as urban mobility and weather patterns, is a crucial yet challenging problem. The complexity stems from the intricate interplay between spatial proximity and temporal relevance, where both long-term trends and short-term fluctuations are present in convoluted patterns. Existing approaches, including traditional statistical methods and conventional neural networks, may provide inaccurate results due to the lack of an effective mechanism that simultaneously incorporates information at variable temporal depths while maintaining spatial context, resulting in a trade-off between comprehensive long-term historical analysis and responsiveness to short-term new information. To bridge this gap, this paper proposes the BiDepth Multimodal Neural Network (BDMNN) with bidirectional depth modulation that enables a comprehensive understanding of both long-term seasonality and short-term fluctuations, adapting to the complex ST context. Case studies with real-world public data demonstrate significant improvements in prediction accuracy, with a 12% reduction in Mean Squared Error for urban traffic prediction and a 15% improvement in rain precipitation forecasting compared to state-of-the-art benchmarks, without demanding extra computational resources.

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