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Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand

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Listed:
  • George Athanasopoulos
  • Ashton de Silva

Abstract

In this paper we propose a new set of multivariate stochastic models that capture time varying seasonality within the vector innovations structural time series (VISTS) framework. These models encapsulate exponential smoothing methods in a multivariate setting. The models considered are the local level, local trend and damped trend VISTS models with an additive multivariate seasonal component. We evaluate their performances for forecasting international tourist arrivals from eleven source countries to Australia and New Zealand.

Suggested Citation

  • George Athanasopoulos & Ashton de Silva, 2010. "Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand," Monash Econometrics and Business Statistics Working Papers 11/09, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2009-11
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2009/wp11-09.pdf
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    References listed on IDEAS

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    13. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    14. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.
    15. Pfeffermann, D. & Allon, J., 1989. "Multivariate exponential smoothing: Method and practice," International Journal of Forecasting, Elsevier, vol. 5(1), pages 83-98.
    16. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
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    Citations

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    Cited by:

    1. Stathis Polyzos & Anestis Fotiadis & Aristeidis Samitas, 2021. "COVID-19 Tourism Recovery in the ASEAN and East Asia Region: Asymmetric Patterns and Implications," Working Papers DP-2021-12, Economic Research Institute for ASEAN and East Asia (ERIA).
    2. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    3. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    4. Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2015. "Forecasting Multivariate Time Series with the Theta Method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 220-229, April.
    5. Elisa Jorge-González & Enrique González-Dávila & Raquel Martín-Rivero & Domingo Lorenzo-Díaz, 2020. "Univariate and multivariate forecasting of tourism demand using state-space models," Tourism Economics, , vol. 26(4), pages 598-621, June.
    6. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    7. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.

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    More about this item

    Keywords

    Holt-Winters’ method; Stochastic seasonality; Vector innovations state space models.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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