GETTING THE MOST OUT OF THE WISDOM OF THE CROWDS: IMPROVING FORECASTING PERFORMANCE THROUGH ENSEMBLE METHODS AND VARIABLE SELECTION TECHNIQUES
Year: 2020

This research focuses on the development of hybrid approaches that combine ensemble-based supervised machine learning techniques and time series methods to obtain accurate forecasts for a wide range of variables and processes. It also includes the development of smart selection heuristics, i.e., procedures that can select, among the pool of forecasts originated via ensemble methods, those with the greatest potential of delivering accurate forecasts after aggregation. Such combinatorial approache