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

• URL: http://arxiv.org/abs/2501.08411v3
• Date: Sun, 13 Jul 2025 16:54:38 GMT
• Title: BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction
• Authors: Sina Ehsani, Fenglian Pan, Qingpei Hu, Jian Liu,
• Abstract summary: This paper proposes the BiDepth Multimodal Neural Network (BDMNN), which integrates two key innovations.<n>BDMNN captures both long-term seasonality and immediate short-term events.<n> CSAC is designed to preserve crucial spatial relationships throughout the network, akin to standard convolutional layers.
• Score: 4.263291797886899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks [53.98633183204453]
  In this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network.<n>A world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling.<n>In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions.
  arXiv  Detail & Related papers  (2025-05-03T06:23:18Z)
• Topology-Aware Conformal Prediction for Stream Networks [54.505880918607296]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework. Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction [25.688521281119037]
  Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is challenging and crucial for optimizing downstream tasks. Traditional prediction approaches focus on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. We propose a novel context-conditionedtemporal predictive learning method to capture dependencies within 4D CSI data.
  arXiv  Detail & Related papers  (2024-09-16T04:15:36Z)
• Navigating Spatio-Temporal Heterogeneity: A Graph Transformer Approach for Traffic Forecasting [13.309018047313801]
  Traffic forecasting has emerged as a crucial research area in the development of smart cities. Recent advancements in network modeling for most-temporal correlations are starting to see diminishing returns in performance. To tackle these challenges, we introduce the Spatio-Temporal Graph Transformer (STGormer) We design two straightforward yet effective spatial encoding methods based on the structure and integrate time position into the vanilla transformer to capture-temporal traffic patterns.
  arXiv  Detail & Related papers  (2024-08-20T13:18:21Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction [13.426775574655135]
  We propose a Multi-Scale Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN. We propose a spatial-temporal synchronous attention mechanism that integrates adaptive position graph convolutions into the self-attention mechanism to achieve synchronous capture of spatial-temporal dependencies. Our model achieves the best prediction accuracy with non-trivial margins compared to all the twenty baseline methods.
  arXiv  Detail & Related papers  (2023-10-12T08:52:36Z)
• STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting [8.596556653895028]
  This study proposes a new spatial-temporal neural network architecture to handle the long-term traffic parameters forecasting issue. The attention mechanism potentially guarantees long-term prediction performance without significant information loss from distant inputs.
  arXiv  Detail & Related papers  (2022-10-01T05:58:22Z)
• Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting [11.680589359294972]
  We propose TrendGCN to address the challenge of balancing dynamics and robustness. Our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts.
  arXiv  Detail & Related papers  (2022-08-05T09:36:55Z)
• STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems [11.167132464665578]
  Short-term passenger flow prediction is an important but challenging task for better managing urban rail transit systems. We propose a novel deep learning-basedtemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy. This study can provide critical experience in conducting short-term passenger flow predictions, especially from the perspective of real-world applications.
  arXiv  Detail & Related papers  (2022-02-10T13:18:11Z)
• Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction [1.2862507359003323]
  We present a novel deep learning architecture that incorporates theories of spatial statistics into neural networks. DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction in a well-fitting, complex physical environment.
  arXiv  Detail & Related papers  (2021-12-10T03:21:19Z)
• Influence Estimation and Maximization via Neural Mean-Field Dynamics [60.91291234832546]
  We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems. Our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
  arXiv  Detail & Related papers  (2021-06-03T00:02:05Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction [0.22940141855172028]
  The proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets. The experimental results show that the proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets.
  arXiv  Detail & Related papers  (2020-05-08T14:23:52Z)
• Interpretable Crowd Flow Prediction with Spatial-Temporal Self-Attention [16.49833154469825]
  The most challenging part of predicting crowd flow is to measure the complicated spatial-temporal dependencies. We propose a Spatial-Temporal Self-Attention Network (STSAN) with an ST encoding gate that calculates the entire spatial-temporal representation. Experimental results on traffic and mobile data demonstrate that the proposed method reduces inflow and outflow RMSE by 16% and 8% on the Taxi-NYC dataset.
  arXiv  Detail & Related papers  (2020-02-22T12:43:11Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.