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

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.