Time series forecasting by global optimization method

Abstract

The actual problem of time series forecasting associated with the choice of the type of forecasting functions is considered. The aim of the work is to develop a new forecasting method, in which, along with the construction of the predictive function, the values of the predicted characteristics are directly optimized at the entire depth of the forecast. The developed method belongs to the class of probabilistic statistical forecast methods and partially to the class of feedback methods. Typical representatives of the methods of these classes are correlation and regression analysis, factor and variance analysis, statistical modeling. In the new method the predictive function is constructed in the basis of trigonometric polynomials, which leads to multi extremality of the forecasting problem. In this regard, to solve the problem of forecasting, the method of global optimization, hereinafter called the criterion shifts method, is used. The developed forecasting method allows solving a wide range of practical forecasting problems. Since the method of global solution search is used, it guarantees the exact finding of the predicted characteristics obtained as a result of their optimization by the criterion of regularity. The results obtained in this article showed high accuracy.