Forecasting Financial and Economic Indicators of Enterprise Performance based on Trend-seasonal Models

Valeriy Yaroslavovich Trofimets, Aleksandr Mikhaylovich Batkovskiy, Kamil Narzavatovich Mingaliev and Igor Vyacheslavovich Bulava

The study examines the theoretical and practical issues of forecasting financial and economic performance of enterprises with a seasonal nature of production. The paper determines the distinctive features of the basic econometric prediction models – additive and multiplicative, based on the nature of seasonal fluctuations. The authors have analyzed and compared the applied models designed to forecast seasonal processes – those include numerous adaptive models, a seasonal version of the auto-regression model of an integrated moving average, and trend-seasonal models. The study examines the advantages and disadvantages of trend-seasonal models compared to other econometric forecasting models addressing its seasonal component. The authors suggest a modified algorithm for constructing trend-seasonal models, which, on the one hand, allows increasing the accuracy of a typical algorithm, and, on the other hand, considers the functionality of modern tabular processors for constructing trend-seasonal models by nonprogrammer users. The practical testing of the proposed algorithm was implemented by predicting the profit of an enterprise with a seasonal nature of production. The authors also conducted a comparative evaluation of the accuracy of the proposed algorithm with a typical algorithm for constructing trend-seasonal models.

Volume 11 | 08-Special Issue

Pages: 935-947