At present, scientists pay great attention to the development and improvement of machine training methods for solving various applied problems. One of such important tasks is the problem of dynamic indicators forecasting for the purpose of increasing the effectiveness of decision-making in the conditions of uncertainty. An undoubted and important characteristic of any forecasting method is the forecast accuracy. One of the most promising and modern trends in improving the forecasting accuracy is the construction of forecasting ensembles.
In this paper, the authors have carried out a study of existing methods for constructing forecasting ensembles in order to justify their use for the problem of interval forecasting. Among such methods, the voting method, the boosting method, the stacking method, the bagging method and the random subspaces method have been considered. Taking into account the specifics of the construction and training of interval forecasting models, the stacking method and the bagging method have been recommended for application. These methods are considered modern, promising and suitable for the interval forecasting models developed by the authors in order to improve the forecasting accuracy.
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