ANALYSIS OF MULTIVARIATE TIME SERIES MODEL AND NEURAL NETWORKS

ANALYSIS OF MULTIVARIATE TIME SERIES MODEL AND NEURAL NETWORKS

ABSTRACT

In this work, vector autoregression and neural network approach to multivariate time series analysis is presented. A multilayer perceptron network with backpropagation, gradient descent algorithm has been designed to model the monthly average exchange rates of three major international currencies with respect to naira. The series span over the period of Jan. 2010 to Aug. 2015. 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 (coRMSE, MBE and R2), it was recorded that the neural network approach performs better than the VAR model as it yielded minimum error of prediction. However, VAR model is recommended for long term prediction of the exchange rates being that it has a smaller mean bias error.