  Temporal aggregation is an intuitively appealing approach to deal with demand
uncertainty. There are two types of temporal aggregation: non-overlapping and
overlapping. Most of the supply chain forecasting literature has focused so far
on the former and there is no research that analyses the latter for
auto-correlated demands. In addition, most of the analytical research to-date
assumes infinite demand series' lengths whereas, in practice, forecasting is
based on finite demand histories. The length of the demand history is an
important determinant of the comparative performance of the two approaches but
has not been given sufficient attention in the literature. In this paper we
examine the effectiveness of temporal aggregation for forecasting finite
auto-correlated demand. We do so by means of an analytical study of the
forecast accuracy of aggregation and non-aggregation approaches based on mean
squared error. We complement this with a numerical analysis to explore the
impact of demand parameters and the length of the series on (comparative)
performance. We also conduct an empirical evaluation to validate the analytical
results using monthly time series of the M4-competition dataset. We find the
degree of auto-correlation, the forecast horizon and the length of the series
to be important determinants of forecast accuracy. We discuss the merits of
each approach and highlight their implications for real world practices.
