Effective machine learning model combination based on selective ensemble strategy for time series forecasting

Abstract

The success of ensemble forecasting heavily depends on the selection and combination of component models as proven by numerous studies that show the superior performance of ensemble forecasting in modeling volatile time series. This work develops a selective machine learning ensemble (SMLE) model that evaluates the validation accuracy of the model ensemble by applying a time series cross-validation method, and establish an efficient model combination process by using a novel soft selection algorithm for selecting and weighing several machine learning models, such as support vector regression, feedforward neural network, random forest, and gradient boosting decision tree. The well-known NN3 time series forecasting experiment and a real-world crude oil price forecasting application are used to verify the performance of SMLE. Numerical results and in-depth analysis indicate that the proposed model can provide more accurate and reliable predictions compared with those obtained via individual forecasts, advanced forecasting techniques, and ensemble strategies.