A COMPARATIVE ANALYSIS OF VECTOR AUTOREGRESSIVE MODEL AND NEURAL NETWORKS

Abstract

In this work, vector autoregression and neural network approach to multivariate time series analysis is presented. A vector autoregressive model and multilayer perceptron network with back-propagation, gradient descent algorithm have been designed to model the monthly average exchange rates of three international currencies with respect to naira. The series span over the period of January, 2012 to August, 2017. The original series were preprocessed to smoothen the distribution and facilitate fast convergence in the network algorithm. In training the network to learn the combined series of the exchange rates, a remarkable achievement was made. Adding to the beauty of the network model is the fact that the number of units of the input layer was predetermined through the VAR model. Using some model performance measures (RMSE, MBE and R²), it was recorded that the neural network approach performs better than the VAR model as it yielded minimum error of prediction.