Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN

This paper proposes the Discrete Wavelet Transform (DWT), Auto Regressive Integrated Moving Averages (ARIMA) model and Recurrent Neural Network (RNN) based technique for forecasting the computer network traffic. Computer network traffic is sampled on computer networking device connected to the inter...

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Published in2018 Eleventh International Conference on Contemporary Computing (IC3) pp. 1 - 5
Main Authors Madan, Rishabh, Mangipudi, Partha Sarathi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
Online AccessGet full text
ISBN1538668343
9781538668344
ISSN2572-6129
DOI10.1109/IC3.2018.8530608

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Abstract This paper proposes the Discrete Wavelet Transform (DWT), Auto Regressive Integrated Moving Averages (ARIMA) model and Recurrent Neural Network (RNN) based technique for forecasting the computer network traffic. Computer network traffic is sampled on computer networking device connected to the internet. At first, discrete wavelet transform is used to decompose the traffic data into non-linear (approximate) and linear (detailed) components. After that, detailed and approximate components are reconstructed using inverse DWT and predictions are made using Auto Regressive Moving Average (ARIMA) and Recurrent Neural Networks (RNN), respectively. Internet traffic is a time series which can be used to predict the future traffic trends in a computer network. Numerous computer network management tasks depend heavily on the information about the network traffic. This forecasting is very useful for numerous applications, such as congestion control, anomaly detection, and bandwidth allocation. Our method is easy to implement and computationally less expensive which can be easily applied at the data centers, improving the quality of service (QoS) and reducing the cost.
AbstractList This paper proposes the Discrete Wavelet Transform (DWT), Auto Regressive Integrated Moving Averages (ARIMA) model and Recurrent Neural Network (RNN) based technique for forecasting the computer network traffic. Computer network traffic is sampled on computer networking device connected to the internet. At first, discrete wavelet transform is used to decompose the traffic data into non-linear (approximate) and linear (detailed) components. After that, detailed and approximate components are reconstructed using inverse DWT and predictions are made using Auto Regressive Moving Average (ARIMA) and Recurrent Neural Networks (RNN), respectively. Internet traffic is a time series which can be used to predict the future traffic trends in a computer network. Numerous computer network management tasks depend heavily on the information about the network traffic. This forecasting is very useful for numerous applications, such as congestion control, anomaly detection, and bandwidth allocation. Our method is easy to implement and computationally less expensive which can be easily applied at the data centers, improving the quality of service (QoS) and reducing the cost.
Author Madan, Rishabh
Mangipudi, Partha Sarathi
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Snippet This paper proposes the Discrete Wavelet Transform (DWT), Auto Regressive Integrated Moving Averages (ARIMA) model and Recurrent Neural Network (RNN) based...
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SubjectTerms ARIMA
Discrete wavelet transforms
DWT
Forecasting
internet traffic
Mathematical model
neural network
Neural networks
prediction
Predictive models
RNN
Telecommunication traffic
time series
Time series analysis
Title Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN
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