# BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction

arXiv: https://arxiv.org/abs/2501.08411 · HTML: https://mlsanity.com/paper/2501.08411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [Latent Space Model for Road Networks to Predict Time-Varying Traffic](https://mlsanity.com/paper/1602.04301/) (2016)
- [Machine Learning for Spatiotemporal Sequence Forecasting: A Survey](https://mlsanity.com/paper/1808.06865/) (2018)
- [Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment](https://mlsanity.com/paper/2012.02175/) (2020)
- [Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting](https://mlsanity.com/paper/2401.03397/) (2024)

## Abstract (arXiv)

Accurate spatial-temporal (ST) prediction for dynamic systems, such as urban mobility and weather patterns, is crucial but hindered by complex ST correlations and the challenge of concurrently modeling long-term trends with short-term fluctuations. Existing methods often falter in these areas. This paper proposes the BiDepth Multimodal Neural Network (BDMNN), which integrates two key innovations: 1) a bidirectional depth modulation mechanism that dynamically adjusts network depth to comprehensively capture both long-term seasonality and immediate short-term events; and 2) a novel convolutional self-attention cell (CSAC). Critically, unlike many attention mechanisms that can lose spatial acuity, our CSAC is specifically designed to preserve crucial spatial relationships throughout the network, akin to standard convolutional layers, while simultaneously capturing temporal dependencies. Evaluated on real-world urban traffic and precipitation datasets, BDMNN demonstrates significant accuracy improvements, achieving a 12% Mean Squared Error (MSE) reduction in urban traffic prediction and a 15% improvement in precipitation forecasting over leading deep learning benchmarks like ConvLSTM, using comparable computational resources. These advancements offer robust ST forecasting for smart city management, disaster prevention, and resource optimization.