Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

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Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects (2014, background): “Andrawis et al. (2011) attempt to predict the daily cash withdrawal amounts from ATM machines.”
Layered Ensemble Architecture for Time Series Forecasting (2016, methodology): “Two main methods for handling seasonality are direct and deseasonalized [41].”
Predictability of monthly temperature and precipitation using automatic time series forecasting methods (2018, background): “Thus, competitions are organized, in which methods are improved and compared (Makridakis et al. 1987; Makridakis and Hibon 2000) and later published to advance science (e.g. Andrawis et al. 2011).”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “• Combination ( COM ) : A combination (average) of the three exponential smoothing methods previously described: SES, Holt and Damped, aimed at achieving the possible beneﬁts of averaging the errors of multiple forecasts (Andrawis et al., 2011).”
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2011, methodology): “Note that we used the same test period and evaluation criterion (i.e. the SMAPE) as used by Andrawis et al in (Andrawis et al., 2011).”