Network traffic prediction method based on autoregressive integrated moving average and adaptive Volterra filter

2021 ◽  
Author(s):  
Zhongda Tian ◽  
Feihong Li
Keyword(s):  
Network Traffic ◽  
Moving Average ◽  
Prediction Method ◽  
Traffic Prediction ◽  
Autoregressive Integrated Moving Average ◽  
Volterra Filter

Joint Network Traffic Forecast with ARIMA Models and Chaotic Models Based on Wavelet Analysis

2011 ◽  
Vol 55-57 ◽  
pp. 743-746 ◽  
Author(s):  
Ming Ke Dong ◽  
Chen Chen ◽  
Min Hua Huang ◽  
Ye Jin
Keyword(s):  
Network Traffic ◽  
Moving Average ◽  
Prediction Method ◽  
Arima Models ◽  
Self Similarity ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Linear Behavior ◽  
Forecast Result ◽  
Wavelet Multiresolution Analysis

In the recent study of network traffic, it is shown that the traffic flow presents both periodic and self-similar characteristics. Due to these two features, the short-term forecast of network traffic cannot be accurately fit in either autoregressive integrated moving average (ARIMA) models which is suitable for linear behavior, or chaotic models which is corresponding to self-similarity characteristic. In this paper, our methodology suggests that by using wavelet multiresolution analysis, we can obtain a joint short-term network traffic prediction method and get a more precise forecast result as compared to using either ARIMA models or chaotic models. We also run simulations to show the improvement of prediction accuracy of our proposed approach.

Prediction Based on PSO-SVM in Power Communication Network Traffic

2014 ◽  
Vol 602-605 ◽  
pp. 2889-2892
Author(s):  
Zhen Dong Zhao ◽  
Rui Ju Xiao ◽  
Meng Meng Pei ◽  
Yi Zhou
Keyword(s):  
Communication Network ◽  
Electric Power ◽  
Network Traffic ◽  
Prediction Method ◽  
Parameters Optimization ◽  
Traffic Prediction ◽  
Model Parameters ◽  
Economy Running ◽  
Important Basis

Power communication network traffic prediction is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. Paper first expounds the electric power communication network traffic prediction research present situation, summarized the characteristics of the forecast and the influencing factors, summarizes the commonly used method, is put forward to the return of the electric power communication network traffic based on libsvm prediction method, and the PSO (particle swarm optimization) algorithm is adopted to model parameters optimization, with the test set error as the decision, based on the optimization of model parameters, choice, makes the prediction precision is improved.

scholarly journals Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Laisen Nie ◽  
Xiaojie Wang ◽  
Liangtian Wan ◽  
Shui Yu ◽  
Houbing Song ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Network Traffic ◽  
Prediction Method ◽  
Deep Belief Network ◽  
Mesh Network ◽  
Traffic Prediction ◽  
Discrete Wavelet ◽  
Belief Network ◽  
Wireless Mesh ◽  
Low Pass

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

scholarly journals Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1305
Author(s):  
Shenghan Zhou ◽  
Bang Chen ◽  
Houxiang Liu ◽  
Xinpeng Ji ◽  
Chaofan Wei ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Moving Average ◽  
Traffic Prediction ◽  
Data Sets ◽  
Prediction Modeling ◽  
Autoregressive Integrated Moving Average ◽  
Smart Transportation ◽  
Characteristics Analysis ◽  
Operational Data

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.

scholarly journals AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huaifeng Shi ◽  
Chengsheng Pan ◽  
Li Yang ◽  
Xiangxiang Gu
Keyword(s):  
Joint Attention ◽  
Network Traffic ◽  
Prediction Method ◽  
Temporal Correlation ◽  
Absolute Error ◽  
Spatial Dimension ◽  
Traffic Prediction ◽  
Intelligent Network ◽  
Time Points ◽  
Temporal Features

Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

scholarly journals Network Traffic Prediction Method Based on Improved Echo State Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 70625-70632 ◽  
Author(s):  
Jian Zhou ◽  
Xinyan Yang ◽  
Lijuan Sun ◽  
Chong Han ◽  
Fu Xiao
Keyword(s):  
Network Traffic ◽  
Prediction Method ◽  
Traffic Prediction ◽  
Echo State Network

scholarly journals Demonstration of an open source platform for reproducible comparison of predictive models

2016 ◽  
Author(s):  
Neeraj Bokde ◽  
Kishore Kulat
Keyword(s):  
Moving Average ◽  
Time Series Prediction ◽  
Prediction Method ◽  
R Package ◽  
Autoregressive Integrated Moving Average ◽  
Imputation Methods ◽  
Time Consumption ◽  
Error Metrics ◽  
Pattern Sequence ◽  
State Of Art

This paper discusses about a tool PredictTestbench, which is an R package which provides a testbench to do comparison of prediction methods. This package compares a proposed time series prediction method with other default methods like Autoregressive integrated moving average (ARIMA) and Pattern Sequence based Forecasting (PSF). The testbench is not limited to these methods. It allows user to add or remove multiple numbers of methods in the existing methods in the study. By default, testbench compares different imputation methods considering different error metrics RMSE, MAE or MAPE. Along with this, it facilitates user to add new error metrics as per requirements. The simplicity of the package usage and significant reduction in efforts and time consumption in state of art procedure, adds valuable advantage to it. The aim of the testbench is reduce the efforts for coding, experiments on output visualization and time for different steps involved in such study. This paper explains the use of all functions in PredictTestbench package with the demonstration of examples.

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