The method of residual-based bootstrap averaging of the forecast ensemble

Abstract 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 approaches, the RBBA method attempts to find optimal weights in and allows for their combination into most effective additive forecast. In method, all forecasts obtain residuals that are statistically terms fitness function residuals. Empirical studies have been conducted demonstrate why how works. The experimental results based on real-world time series contemporary stock exchanges show can produce with good generalization ability.