Hierarchical forecasts for Australian domestic tourism
George Athanasopoulos (),
Roman A. Ahmed and
Rob Hyndman ()
International Journal of Forecasting, 2009, vol. 25, issue 1, pages 146-166
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.
Keywords: Australia; Exponential; smoothing; Hierarchical; forecasting; Innovations; state; space; models; Optimal; combination; forecasts; Top-down; method; Tourism; demand (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (19) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Hierarchical forecasts for Australian domestic tourism (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:eee:intfor:v:25:y:2009:i:1:p:146-166
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Series data maintained by Zhang, Lei ().