Beschreibung:
This paper presents an optimization approach-residual-based bootstrap averaging (RBBA)-for different types of forecast ensembles. Unlike traditional residual-mean-square-error-based ensemble forecast averaging approaches, the RBBA method attempts to find optimal forecast weights in an ensemble and allows for their combination into the most effective additive forecast. In the RBBA method, all the different types of forecasts obtain the optimal weights for ensemble residuals that are statistically optimal in terms of the fitness function of the residuals. Empirical studies have been conducted to demonstrate why and how the RBBA method works. The experimental results based on the real-world time series of contemporary stock exchanges show that the RBBA method can produce ensemble forecasts with good generalization ability.