Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

This paper discusses the application of ensemble techniques for the prediction of time series, presenting an in-depth review of the main techniques and algorithms used by the recent literature, with emphasis on the bootstrap aggregation (bagging) and boosting approaches. We also discuss the theoretical foundations of the ensemble-based models, presenting measures of model stability and the main aggregation methods to combine the forecasts of the individual models, as well as recommendations for future developments for related research agendas.