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Unmasking the Theta Method

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Author Info
Hyndman, R.J. ()
Billah, B.

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Abstract

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.

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File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2001/wp5-01.pdf
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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 5/2001.

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Length: 6 pages
Date of creation: Jun 2001
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Handle: RePEc:msh:ebswps:2001-5

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Related research
Keywords: exponential smoothing; forecasting competitions; state space models;

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Find related papers by JEL classification:
C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
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.:
  1. 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. [Downloadable!] (restricted)
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  2. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530. [Downloadable!] (restricted)
  3. 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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Cited by:
(explanations, 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.)

  1. Md B. Billah & R.J. Hyndman & A.B. Koehler, 2003. "Empirical Information Criteria for Time Series Forecasting Model Selection," Monash Econometrics and Business Statistics Working Papers 2/03, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Rob J Hyndman & Maxwell L. King & Ivet Pitrun & Baki Billah, 2002. "Local Linear Forecasts Using Cubic Smoothing Splines," Monash Econometrics and Business Statistics Working Papers 10/02, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  3. George Athanasopoulos & Rob J Hyndman & Haiyan Song & Doris C Wu, 2008. "The tourism forecasting competition," Monash Econometrics and Business Statistics Working Papers 10/08, Monash University, Department of Econometrics and Business Statistics, revised Oct 2009. [Downloadable!]
  4. Yeasmin Khandakar & Rob J. Hyndman, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, American Statistical Association, vol. 27(03), 07. [Downloadable!]
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