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The tourism forecasting competition

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Author Info
George Athanasopoulos ()
Rob J Hyndman ()
Haiyan Song
Doris C Wu

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Abstract

We evaluate the performance of various methods for forecasting tourism demand. The data used include 380 monthly series, 427 quarterly series and 530 yearly series, all supplied to us by tourism bodies or by academics from previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate time series approaches, and also econometric models. This forecasting completion differs from previous competitions in several ways: (i) we concentrate only on tourism demand data; (ii) we include econometric approaches; (iii) we evaluate forecast interval coverage as well as point forecast accuracy; (iv) we observe the effect of temporal aggregation on forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure.

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File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2008/wp10-08.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 10/08.

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Length: 34 pages
Date of creation: Dec 2008
Date of revision: Oct 2009
Handle: RePEc:msh:ebswps:2008-10

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Related research
Keywords: Tourism forecasting; ARIMA; Exponential smoothing; Time varying parameter model; Autoregressive distributed lag model; Vector autoregression;

Other versions of this item:

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
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. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476. [Downloadable!] (restricted)
  2. 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)
    Other versions:
  3. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September. [Downloadable!] (restricted)
  4. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725. [Downloadable!] (restricted)
  5. JS Armstrong, 2004. "Should We Redesign Forecasting Competitions?," General Economics and Teaching 0412001, EconWPA. [Downloadable!]
  6. 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)
  7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688. [Downloadable!] (restricted)
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  8. Goodrich, Robert L., 2000. "The Forecast Pro methodology," International Journal of Forecasting, Elsevier, vol. 16(4), pages 533-535. [Downloadable!] (restricted)
  9. Bohara, Alok K & Sauer, Christine, 1992. "Competing Macro-hypotheses in the United States: A Kalman Filtering Approach," Applied Economics, Taylor and Francis Journals, vol. 24(4), pages 389-99, April.
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  11. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society. [Downloadable!]
  12. Hyndman, Rob J. & Billah, Baki, 2003. "Unmasking the Theta method," International Journal of Forecasting, Elsevier, vol. 19(2), pages 287-290. [Downloadable!] (restricted)
    Other versions:
  13. Greenslade, Jennifer V. & Hall, Stephen G., 1996. "Modelling economies subject to structural change: The case of Germany," Economic Modelling, Elsevier, vol. 13(4), pages 545-559, October. [Downloadable!] (restricted)
  14. Anne B. Koehler & Rob J. Hyndman & Ralph D. Snyder & J. Keith Ord, 2005. "Prediction intervals for exponential smoothing using two new classes of state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 17-37. [Downloadable!]
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