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Working Papers 1999 – Abstracts

9/99 Forecasting Time Series From Clusters Elizabeth A. Maharaj and Brett A. Inder: Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting a large number of series that are logically connected in some way, we can first cluster them into groups of similar series. In this paper we investigate forecasting the series in each cluster. Similar series are first grouped together using a clustering procedure that is based on a test of hypothesis. The series in each cluster are then pooled together and forecasts are obtained. Simulated results show that this procedure for forecasting similar series performs reasonably well.
Keywords: Autoregressive models, clustering technique, mean square forecast error , pooled series

10/99 Forecasting for Inventory Control with Exponential Smoothing Ralph D. Snyder, Anne Koehler and Keith Ord: Exponential smoothing, often used for sales forecasting in inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains the appropriate approach under more general conditions where the variances are allowed to grow and contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular the problem of finding the prediction distribution of aggregate lead- time demand for use in inventory control calculations is considered. It is found that unless a drift term is added to simple exponential smoothing, the prediction distribution is largely unaffected by the variance assumption. A method for establishing order-up-to levels and reorder levels directly from the simulated prediction distributions is also proposed.
Keywords: Inventory control, demand forecasting, exponential smoothing, bootstrap methods