Forecasting Trends in Time Series

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Forecasting Trends in Time Series Author(s): Everette S. Gardner, Jr. and Ed. McKenzie Reviewed work(s): Source: Management Science, Vol. 31, No. 10 (Oct., 1985), pp. 1237-1246 Published by: INFORMS Stable URL: . Accessed: 20/12/2012 02:05 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .

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MANAGEMENT SCIENCE Vol. 31, No. 10, October 1985 Printed in U.S.A.

EVERETTE S. GARDNER, JR. AND ED. McKENZIE OperationsAnalysis Department,Navy Fleet Material SupportOffice, P.O. Box 2010, Mechanicsburg,Pennsylvania 17055 Mathematics Department, Universityof Strathclyde, Glasgow GI 1XW, Scotland, United Kingdom Most time series methods assume that any trend will continue unabated, regardless of the forecast leadtime. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence. This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1,001 time series first analyzed by Makridakis et al. Compared to smoothing models based on a linear trend, the model improves forecast accuracy, particularly at long leadtimes. The model also compares favorably to...
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