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Forecast Selection and Representativeness., and . Manag. Sci., 69 (5): 2672-2690 (May 2023)Improving forecasting by subsampling seasonal time series., , and . Int. J. Prod. Res., 61 (3): 976-992 (February 2023)Hierarchical forecast reconciliation with machine learning., , , , and . Appl. Soft Comput., (2021)An aggregate-disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis., , , , and . JORS, 62 (3): 544-554 (2011)Old dog, new tricks: a modelling view of simple moving averages., and . Int. J. Prod. Res., 56 (18): 6034-6047 (2018)Exploring the sources of uncertainty: Why does bagging for time series forecasting work?, , and . Eur. J. Oper. Res., 268 (2): 545-554 (2018)'Horses for Courses' in demand forecasting., , , and . Eur. J. Oper. Res., 237 (1): 152-163 (2014)Probabilistic forecast reconciliation with applications to wind power and electric load., , and . Eur. J. Oper. Res., 279 (2): 364-379 (2019)Understanding forecast reconciliation., , and . Eur. J. Oper. Res., 294 (1): 149-160 (2021)Do 'big losses' in judgmental adjustments to statistical forecasts affect experts' behaviour?, , and . Eur. J. Oper. Res., 249 (3): 842-852 (2016)