USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS

Abstract

Modern economic science is unthinkable without predicting and planning the prospects for economic life development. There are many different mathematical and statistical tools in the arsenal of scientists as well as practitioners and economists today in purpose of forecasting. To date, one of the most prominent effective tools for data analytics is artificial neural networks. Artificial Neural Network - is a mathematical mod- el created in the likeness of a human neural network, and its software and hardware implementation. We carried out modeling and forecasting of regional economic indicators using the artificial neural network of the three-layer perceptron architecture. The network architecture and neuron settings were automatically formatted through the programming language R and its package - Neuralnet. During the forecasting phase, the data vectors were presented as data frame in five input parameters (DFI, FAI, EMP, BT, CPI), according to the neural network forecast of the regional gross domestic product (RGDP_NN) was calculated. All data are from the Imereti region and are taken from official GeoStat sources. Forecasting was done at the same time scale (2006-2017) to enable us to compare the predicted values with the actual ones to verify the level of fore- cast accuracy. We also tested the results of the neural network in another way - compared to the predicted values using multiple linear regression on the same data. The accuracy of the predicted values calculated by the neural network was quite high, which was not declining but slightly ahead of the accuracy coefficients of the predicted values obtained through linear regression. Also, the predictive values calculated by the neural network with high adequacy and accuracy were compared with actual, existing ones. Presented material shows that the use of artificial neural networks for the prediction of territorial economic indicators is reasonable and justified. Their role in analyzing and predicting indicators that are characterized by nonstationarity, dynamism, lack of a definite trend, periodicity, nonlinear structure is especially increased. It is therefore advisable to apply this method in regional economic studies, in predicting territorial development plans, strategies, targets and indicators.