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Hierarchical forecasts for Australian domestic tourism

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  • Athanasopoulos, George
  • Ahmed, Roman A.
  • Hyndman, Rob J.

Abstract

In this paper we explore the hierarchical nature of tourism demand time series and produce short-term forecasts for Australian domestic tourism. The data and forecasts are organized in a hierarchy based on disaggregating the data according to geographical regions and purposes of travel. We consider five approaches to hierarchical forecasting: two variations of the top-down approach, the bottom-up method, a newly proposed top-down approach where top-level forecasts are disaggregated according to the forecasted proportions of lower level series, and a recently proposed optimal combination approach. Our forecast performance evaluation shows that the top-down approach based on forecast proportions and the optimal combination method perform best for the tourism hierarchies we consider. By applying these methods, we produce detailed forecasts of the Australian domestic tourism market.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:25:y:2009:i:1:p:146-166
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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, pages 451-476.
    2. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, pages 2579-2589.
    3. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
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    11. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, pages 233-241.
    12. 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, pages 439-454.
    13. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-214, July.
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    Cited by:

    1. Divino, J.A. & McAleer, M.J., 2008. "Modelling sustainable international tourism demand to the Brazilian Amazon," Econometric Institute Research Papers EI 2008-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    3. Divino, Jose Angelo & McAleer, Michael, 2010. "Modelling and forecasting daily international mass tourism to Peru," Tourism Management, Elsevier, vol. 31(6), pages 846-854.
    4. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, pages 60-74.
    5. repec:eee:touman:v:32:y:2011:i:5:p:1075-1084 is not listed on IDEAS
    6. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, pages 20-32.
    7. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    8. Deng, Minfeng & Athanasopoulos, George, 2011. "Modelling Australian domestic and international inbound travel: a spatial–temporal approach," Tourism Management, Elsevier, vol. 32(5), pages 1075-1084.
    9. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    10. 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.
    11. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.
    12. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, pages 822-844.
    13. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    14. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, pages 2579-2589.
    15. Shanika L Wickramasuriya & George Athanasopoulos & Rob J Hyndman, 2015. "Forecasting hierarchical and grouped time series through trace minimization," Monash Econometrics and Business Statistics Working Papers 15/15, Monash University, Department of Econometrics and Business Statistics.
    16. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), pages 55-84.
    17. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    18. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, pages 16-32.
    19. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, pages 2579-2589.
    20. Emilio Dominguez & Alfonso Novales, 2002. "A factor model of term structure slopes in Eurocurrency markets," Applied Economics Letters, Taylor & Francis Journals, pages 585-593.
    21. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    22. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, pages 415-444.
    23. repec:eee:ejores:v:263:y:2017:i:2:p:412-418 is not listed on IDEAS

    More about this item

    Keywords

    Australia Exponential smoothing Hierarchical forecasting Innovations state space models Optimal combination forecasts Top-down method Tourism demand;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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