Exponential Smoothing Model Selection for Forecasting
Applications of exponential smoothing to forecast time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approach appears to provide the best basis for an automated approach to method selection, provided that it is based on Akaike's information criterion.
|Date of creation:||Mar 2005|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.buseco.monash.edu.au/depts/ebs/
More information through EDIRC
|Order Information:|| Web: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/ Email: |
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Hyndman, R.J. & Koehler, A.B. & Snyder, R.D. & Grose, S., 2000.
"A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods,"
Monash Econometrics and Business Statistics Working Papers
9/00, Monash University, Department of Econometrics and Business Statistics.
- Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
- Bartolomei, Sonia M. & Sweet, Arnold L., 1989. "A note on a comparison of exponential smoothing methods for forecasting seasonal series," International Journal of Forecasting, Elsevier, vol. 5(1), pages 111-116.
- Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
- Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
- Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:2005-6. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Simone Grose)
If references are entirely missing, you can add them using this form.