Internet Traffic Forecasting using Temporal-Topological Graph Convolutional Networks

2021 ◽  
Author(s):  
Zhenjie Yao ◽  
Qian Xu ◽  
Yongrui Chen ◽  
Yanhui Tu ◽  
He Zhang ◽  
...  
Keyword(s):  
Internet Traffic ◽  
Topological Graph ◽  
Traffic Forecasting ◽  
Convolutional Networks

An Improved LS-SVR Ensemble Learning in Internet Traffic Prediction

2011 ◽  
Vol 121-126 ◽  
pp. 3794-3798 ◽  
Author(s):  
Kun Lun Li ◽  
Ying Hui Ma ◽  
Yong Mei Tian ◽  
Jing Xie
Keyword(s):  
Time Series ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Internet Traffic ◽  
Traffic Prediction ◽  
Initial Training ◽  
Classification Problems ◽  
Traffic Forecasting ◽  
Ensemble Learning Algorithm

In this paper, we present a new method for internet traffic forecasting based on a boosting LS-SVR algorithm. AdaBoost has been proved to be an effective method for improving the performance of weak learning algorithms and widely applied to classification problems. Inspired by it, we use LS-SVR to complete the initial training; and pay more attention on the “high error areas” in the time series; then, we use an ensemble learning algorithm to learn these areas.

scholarly journals DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1014
Author(s):  
Chengsheng Pan ◽  
Jiang Zhu ◽  
Zhixiang Kong ◽  
Huaifeng Shi ◽  
Wensheng Yang
Keyword(s):  
Feature Extraction ◽  
Network Traffic ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Temporal Characteristics ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Forecasting Models ◽  
Dual Channel

Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily and weekly correlation of the network traffic. Each of these two components contains a spatial-temporal characteristics extraction module consisting of a dual-channel graph convolutional network (DCGCN) and a gated recurrent unit (GRU). The DCGCN further consists of an adjacency feature extraction module (AGCN) and a correlation feature extraction module (PGCN) to capture the connectivity between nodes and the proximity correlation, respectively. The GRU further extracts the temporal characteristics of the traffic. The experimental results based on real network data sets show that the prediction accuracy of the DC-STGCN model overperforms the existing baseline and is capable of making long-term predictions.

scholarly journals Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

2018 ◽  
Author(s):  
Bing Yu ◽  
Haoteng Yin ◽  
Zhanxing Zhu
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Time Series Prediction ◽  
Urban Traffic ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Temporal Correlations ◽  
Temporal Graph ◽  
Spatio Temporal

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

scholarly journals Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

2020 ◽  
Vol 34 (04) ◽  
pp. 3529-3536 ◽  
Author(s):  
Weiqi Chen ◽  
Ling Chen ◽  
Yu Xie ◽  
Wei Cao ◽  
Yusong Gao ◽  
...  
Keyword(s):  
Road Network ◽  
Weighted Graph ◽  
Interaction Patterns ◽  
Convolutional Network ◽  
The Road ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Traffic Conditions ◽  
Aggregate Information ◽  
Deep Learning Model

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.

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