Unmasking the Theta Method
The Theta method of forecasting performed particularly well in the M3-competition and is therefore of interest to forecast practitioners. The description of the method given by Assimakopoulos and Nikolopoulos (2000) involves several pages of algebraic manipulation and is difficult to comprehend. We show that the method can be expressed much more simply; furthermore we show that the forecasts obtained are equivalent to simple exponential smoothing with drift.
|Date of creation:||Jun 2001|
|Date of revision:|
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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.
- Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
- Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
- 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.
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