Forecasting Trends in Time Series
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: http://www.jstor.org/stable/2631713 . Accessed: 20/12/2012 02:05
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.
.
INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Management Science.
http://www.jstor.org
This content downloaded on Thu, 20 Dec 2012 02:05:27 AM All use subject to JSTOR Terms and Conditions
MANAGEMENT SCIENCE Vol. 31, No. 10, October 1985 Printed in U.S.A.
FORECASTING TRENDS IN TIME SERIES*
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
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.
.
INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Management Science.
http://www.jstor.org
This content downloaded on Thu, 20 Dec 2012 02:05:27 AM All use subject to JSTOR Terms and Conditions
MANAGEMENT SCIENCE Vol. 31, No. 10, October 1985 Printed in U.S.A.
FORECASTING TRENDS IN TIME SERIES*
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