Graph Neural Controlled Differential Equations for Traffic Forecasting

• URL: http://arxiv.org/abs/2112.03558v1
• Date: Tue, 7 Dec 2021 08:14:10 GMT
• Title: Graph Neural Controlled Differential Equations for Traffic Forecasting
• Authors: Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park
• Abstract summary: Traffic is one of the most popular-temporal tasks in the field of machine learning. In this paper we present the method of graph neural controlled differential equations (NCDEs) We extend the concept to design two NCDEs: one for the temporal processing and the other for the spatial processing.
• Score: 4.012886243094023
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this paper, we present the method of spatio-temporal graph neural controlled differential equation (STG-NCDE). Neural controlled differential equations (NCDEs) are a breakthrough concept for processing sequential data. We extend the concept and design two NCDEs: one for the temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 20 baselines. STG-NCDE shows the best accuracy in all cases, outperforming all those 20 baselines by non-trivial margins.

Related papers

• Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting [8.832864937330722]
  Long-range traffic forecasting remains a challenging task due to the intricate and extensive-temporal correlations observed in traffic networks. In this paper, we propose a architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations. Our extensive set of experiments conducted on six real-world datasets demonstrate the superior performance of GRAM-ODE compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-05-30T02:10:42Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Graph Neural Rough Differential Equations for Traffic Forecasting [12.920211720852173]
  We present the rough method of graph neural differential equation (STG-NRDE) NRDE is a breakthrough concept for processing time-series data. We conduct experiments with 6 benchmark datasets and 27 baselines.
  arXiv  Detail & Related papers  (2023-03-20T06:57:10Z)
• Semi-supervised Learning of Partial Differential Operators and Dynamical Flows [68.77595310155365]
  We present a novel method that combines a hyper-network solver with a Fourier Neural Operator architecture. We test our method on various time evolution PDEs, including nonlinear fluid flows in one, two, and three spatial dimensions. The results show that the new method improves the learning accuracy at the time point of supervision point, and is able to interpolate and the solutions to any intermediate time.
  arXiv  Detail & Related papers  (2022-07-28T19:59:14Z)
• Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting [4.1700160312787125]
  We propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block.
  arXiv  Detail & Related papers  (2022-06-07T09:08:35Z)
• EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting [19.37382379378985]
  neural controlled differential equations (NCDEs) are considered as a breakthrough in deep learning. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. Our NCDE design can use both the extrapolation and the extrapolated information for downstream machine learning tasks.
  arXiv  Detail & Related papers  (2022-04-19T09:37:36Z)
• Continual Spatio-Temporal Graph Convolutional Networks [87.86552250152872]
  We reformulating the Spatio-Temporal Graph Convolutional Neural Network as a Continual Inference Network. We observe up to 109x reduction in time complexity, on- hardware accelerations of 26x, and reductions in maximum allocated memory of 52% during online inference.
  arXiv  Detail & Related papers  (2022-03-21T14:23:18Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting [3.673363968661099]
  We present the method of Attentive Neural Controlled Differential Equations (ANCDEs) for time-series classification and forecasting. Our method consistently shows the best accuracy in all cases by non-trivial margins. Our visualizations also show that the presented attention mechanism works as intended by focusing on crucial information.
  arXiv  Detail & Related papers  (2021-09-04T14:17:01Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)

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.