Combination of several algorithms while solving the forecasting problem

The forecasting problem is undoubtedly one of the most important for humanity, but also it is standing next to the most difficult ones because there is no guarantee that the predicted process behavior will not change dramatically in the future. The aim of the paper is to develop a new forecasting method that uses an approach of several models combining and test its quality on a set of test data. For this purpose, the main existing forecasting methods were considered, such as artificial neural networks, group method of data handling and linear regression. For the combination of forecasting models the bagging approach was chosen. The proposed method was implemented in a software environment Matlab and tested (along with several existing methods) on 11 sets of test data. The fact that among the methods that were tested, the proposed one showed the best results, indicating the possibility of its successful application in practice is a scientific novelty and practical significance of the article.