Forecasting Abilities of MIMO and SISO Neural Networks: A Comparative Study using Telecommunication Traffic Data

Abstract

The study compared the forecasting performance of two multiple-input and multiple-output (MIMO) and two single-input and single-output (SISO) neural networks using 4G network traffic data aggregated into daily, weekly and monthly time spans. To explore the best configuration of SISO and MIMO neural networks, the empirical traffic data of 1-input, 2- input and 3-input were used together with varying the parameters of the models. The study concluded that for 2-input, MIMO Radial basis function neural (RBFN) network performed better than the 2-input MIMO Multilayer perceptron (MLP) neural network in predicting the traffic data. In the case of 3-input, MLP network was found to be more efficient than RBFN network. In the scenario of SISO architecture, the MLP network outperformed the RBFN network for 4G daily, weekly and monthly traffic data.