hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottom …-up approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a … also allows us to construct prediction intervals for the resultant forecasts. Finally, we apply the method to forecasting …

forecasting. The parameter space for SSOE models may be specified to match that of the corresponding ARIMA scheme, or it may be … that underlies the Holt-Winters forecasting method. Conditionally heteroscedastic models may be developed in a similar …

to be forecast. The EIC provides a data-driven model selection tool that can be tuned to the particular forecasting task …'s Bayesian Information Criterion (BIC). The comparisons show that for the M3 forecasting competition data, the EIC outperforms …

the other periods. Croston's method is a widely used procedure for intermittent demand forecasting. However, it is an ad …

assumed to be Gaussian, the resulting prediction distribution may have an infinite variance beyond a certain forecasting … approximation causes no serious problems for parameter estimation or for forecasting one or two steps ahead. However, for longer …. The performance of the Gaussian approximation is compared with those of two lognormal models for short-term forecasting …

forecasting error (MISFE) and accounts for 99.3% of variation around the mean mortality curve. 20 year forecast suggest a … greatest decline in older women. We illustrate the utility of a new modelling and forecasting approach to model breast cancer …