Internet Traffic Forecasting using Boosting LSTM Method

Abstract

As Internet traffic is a kind of time series, the algorithms which are suitable for time series forecasting are also appropriate for Internet traffic. We study approaches for time series prediction firstly and then apply the algorithms to Internet traffic prediction in this paper. We present an ensemble method based on Long Short-Term Memory (LSTM) Network method for time series prediction with the aim of increasing the prediction accuracy of Internet traffic. We use AdaBoost algorithm to boost LSTM method because AdaBoost algorithm is the most widely used boosting algorithm. However, original AdaBoost algorithm is not suitable for prediction, in view of this fact, we use modified AdaBoost algorithm to be combined with LSTM in this paper. We use two data sets to experiment, and the results show that AdaBoost-LSTM algorithm is more accurate than single LSTM algorithm and even better than ARIMA in some situations. At last, we apply AdaBoost-LSTM algorithm to forecast the Internet traffic.