Applied Statistics

650

Research

L.S.-Y. Wu, J.R.M. Hosking and N. Ravishankar, 1993, Reallocation outliers in time series,

Applied Statistics, 42, 301-313. Outliers are a common problem in time series and methods which deal with them either directly, by robust estimation, or indirectly, through adjusting the data series are an important tool in the forecaster’s armoury. The authors define a new class of outlier, which they call a reallocation where a block of unusually high observations is followed by a low block (or vice versa) and where the sum of these observations remains undisturbed. Such a phenomena is common, for example, over the period of a sales promotion and its aftermath. The authors adopt an ARIMA modelling framework and first propose tests for Reallocation outliers. Two important cases are included: where the timing of the reallocation is

on forecasting

unknown and where the duration is unknown. (In many applications, such as the retrospective analysis of the effects of a promotion both would be known approximately.) The authors then consider the effects on forecasting. Based on two case studies they conclude that while reallocation outliers often don’t have much effect on the forecast values (except for those few forecasts which give them substantial weight), “the presence of reallocation outliers can cause excessively wide confidence intervals for all time periods”. -Robert Fildes [Lilian S-Y Wu, IBM Research Division, Watson Research Center, PO Box 218, ktown Heights, NY 10598, USA] SSDZ

0169-2070(94)00567-2

T.J. Yor-