Transformations  in
Business & Economics

NEURAL NETWORKS VERSUS BOX-JENKINS METHOD FOR TURNOVER FORECASTING: A CASE STUDY ON THE ROMANIAN ORGANISATION
Manuela Rozalia Gabor, Lavinia Ancuta Dorgo

ABSTRACT. Based on the information from the balance sheet and the profit and loss account, the management of a company can make a series of economic decisions. However, in most cases, financial statements represent the information staggered in time, thus, the management needs to perform forecasts by means of statistical, econometrical or artificial intelligence tools in order to substantiate its decision. Using the results of the forecast, the management has the possibility to compare the real results to the forecasted ones, to identify their deviations, study the causes and elaborate efficient policies and strategies. The intelligent system and statistical tools can act as preventive elements, capable of signalling significant deviations, being a real "guardian" of the business. Thus, the paper presents a comparative applicative study of two methods, namely the Box-Jenkins method and neural networks for forecasting the turnover of a company engaged in manufacturing and exporting in the wood industry .

KEYWORDS:  forecast, turnover, Box-Jenkins method, neural networks, decision, Romania.

JEL classification:  C01, C22, C45, C81.