Hierarchical forecasts for Australian domestic tourism

By George Athanasopoulos, R. Ahmed, Rob J Hyndman. 2009

273 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Sociology and Political Science, Tourism, Leisure and Hospitality Management

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Citations in the corpus

Optimal combination forecasts for hierarchical time series (2011, methodology): “A more detailed analysis of these data was provided in Athanasopoulos et al. (2009).”
The Tourism Forecasting Competition (2011, background): “http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/”
'Horses for Courses' in demand forecasting (2014, background): “Lastly, Athanasopoulos et al. (2009) explore different approaches to hierarchical forecasting, proposing an “optimal” approach, which provides reconciled forecasts at every level.”
Forecasting hierarchical and grouped time series through trace minimization (2015, methodology): “See Athanasopoulos, Ahmed, and Hyndman (2009) for further discussion of P matrices for various hierarchical forecasting methods.”
Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, background): “…series model for domestic Australian tourism, using a state space approach, while for the same problem of forecasting domestic Australian tourism, Athanasopoulos, Ahmed, and Hyndman (2009) considered a hierarchical forecasting model based on disaggregating the data for different geographical…”
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, background): “A recent application of global multivariate models is for hierarchical forecasting problems [17, 191, 176, 7].”
Energy Forecasting: A Review and Outlook (2020, methodology): “Recent developments of hierarchical forecasting were mostly contributed by Hyndman’s research group [45].”
Forecasting: theory and practice (2020, background): “(2011) and Athanasopoulos et al. (2009), and work that followed from these (particularly Wickramasuriya et al.”
Forecasting with temporal hierarchies (2017, methodology): “This method is used by Athanasopoulos et al. (2009) with good empirical results and further developed by Wickramasuriya et al. (2015).”
Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018, unknown): “For further discussion of P matrices for various hierarchical forecasting methods, see Athanasopoulos, Ahmed, and Hyndman (2009).”