An approach to combining forecasts when solving machine learning problems

Abstract

The article investigates an approach to solving forecasting problems based on a combination of forecast solutions. A structural diagram of a forecasting approach using a combination of forecasts is proposed. An information system architecture is developed to improve the efficiency of forecasting based on combined forecasts. The task of forecasting electricity demand in Ukraine is considered as an example. A time series reflecting electricity demand in the period from 2019 to 2024 was studied. A structural diagram of a forecasting approach based on combining forecasts is developed. The scheme considers as basic methods of forecasting time series based on machine learning methods, namely: generalized additive model, exponential smoothing model, ARIMA model and neural network autoregression model. For each method, several models were built, the accuracy of which was evaluated on the training and test samples, then the optimal model was selected, thus 4 independent models were obtained. Several methods of combining forecasts were considered. To solve the forecasting problem, seven forecast combination methods were applied to obtain combined forecasts from the forecasts of individual models. The combination methods demonstrated an improvement in forecast accuracy compared to the best models. Among them, the simple averaging method has the highest accuracy. The proposed approach is effective in solving machine learning problems.