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Cross-temporal coherent forecasts for Australian tourism

Author

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  • Nikolaos Kourentzes
  • George Athanasopoulos

Abstract

Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be `coherent', that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources.

Suggested Citation

  • Nikolaos Kourentzes & George Athanasopoulos, 2018. "Cross-temporal coherent forecasts for Australian tourism," Monash Econometrics and Business Statistics Working Papers 24/18, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2018-24
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp24-2018.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. George Athanasopoulos & Nikolaos Kourentzes, 2021. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 10/21, Monash University, Department of Econometrics and Business Statistics.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Eckert, Florian & Hyndman, Rob J. & Panagiotelis, Anastasios, 2021. "Forecasting Swiss exports using Bayesian forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 693-710.
    4. Kourentzes, Nikolaos & Saayman, Andrea & Jean-Pierre, Philippe & Provenzano, Davide & Sahli, Mondher & Seetaram, Neelu & Volo, Serena, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Annals of Tourism Research, Elsevier, vol. 88(C).
    5. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    6. Panagiotelis, Anastasios & Athanasopoulos, George & Gamakumara, Puwasala & Hyndman, Rob J., 2021. "Forecast reconciliation: A geometric view with new insights on bias correction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 343-359.
    7. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
    8. Leprince, Julien & Madsen, Henrik & Møller, Jan Kloppenborg & Zeiler, Wim, 2023. "Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads," Applied Energy, Elsevier, vol. 348(C).
    9. Hollyman, Ross & Petropoulos, Fotios & Tipping, Michael E., 2021. "Understanding forecast reconciliation," European Journal of Operational Research, Elsevier, vol. 294(1), pages 149-160.
    10. Lila, Maurício Franca & Meira, Erick & Cyrino Oliveira, Fernando Luiz, 2022. "Forecasting unemployment in Brazil: A robust reconciliation approach using hierarchical data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
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    12. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
    13. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
    14. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    15. George Athanasopoulos & Nikolaos Kourentzes, 2020. "On the Evaluation of Hierarchical Forecasts," Monash Econometrics and Business Statistics Working Papers 2/20, Monash University, Department of Econometrics and Business Statistics.
    16. Athanasopoulos, George & Kourentzes, Nikolaos, 2023. "On the evaluation of hierarchical forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1502-1511.
    17. Di Fonzo, Tommaso & Girolimetto, Daniele, 2023. "Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives," International Journal of Forecasting, Elsevier, vol. 39(1), pages 39-57.
    18. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    19. Daniele Girolimetto & George Athanasopoulos & Tommaso Di Fonzo & Rob J Hyndman, 2023. "Cross-temporal Probabilistic Forecast Reconciliation," Monash Econometrics and Business Statistics Working Papers 6/23, Monash University, Department of Econometrics and Business Statistics.
    20. Panagiotelis, Anastasios & Gamakumara, Puwasala & Athanasopoulos, George & Hyndman, Rob J., 2023. "Probabilistic forecast reconciliation: Properties, evaluation and score optimisation," European Journal of Operational Research, Elsevier, vol. 306(2), pages 693-706.
    21. Davide Provenzano & Serena Volo, 2022. "Tourism recovery amid COVID-19: The case of Lombardy, Italy," Tourism Economics, , vol. 28(1), pages 110-130, February.
    22. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2020. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," Applied Energy, Elsevier, vol. 261(C).
    23. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    24. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).

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