Development of a new software for network traffic prediction and forecasting using time series multilayer perceptron and feedback delays

Accurate prediction of Internet network traffic plays an important role both in academical literature and the Internet network industry. World-known corporations like Internet Service Providers (ISP), hosting companies and all types of web sites can benefit from the prediction of the amount of network data usage by arranging their business plans according to the customers' needs and preferences. In this thesis, it was aimed to develop a new software that can predict the Internet data traffic using Multilayer Perceptron (MLP) combined with Time Series Analysis. The software has been developed using MATLAB programming language. Two different prediction modes have been integrated into the software, including 'Predict' (open-loop prediction) and 'Forecast' (closed-loop prediction). The prediction models have been evaluated with respect to their Mean Absolute Percentage Error (MAPE) values. As a result, it has been proven that this software can be used for network traffic prediction, producing acceptable error rates under certain circumstances.