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.

Highlights

  • Tourism demand is measured by the number of “visitor nights”, the total nights spent away from home

  • Australia can be divided into six states: New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA) and Tasmania (TAS), and the Northern Territory (NT). (For the purposes of this analysis, we treat the Australian Capital Territory as part of NSW and refer to the Northern Territory as a “state”.) Business planners require forecasts for the whole of Australia, for each state, and for smaller regions

  • At level 1 there is a strong downward trend for the New South Wales series which comprises 33% of the total tourism demand for Australia. This trend is captured by both the top-down method based on forecasted proportions and the optimal combination approach

Read more

Summary

Introduction

Tourism demand is measured by the number of “visitor nights”, the total nights spent away from home. The data is disaggregated by geographical region and by purpose of travel, forming a natural hierarchy of quarterly time series. In this paper we take advantage of this hierarchical structure, using hierarchical forecasting methods to produce forecasts for several levels of disaggregation for the Australian domestic tourism market. First we propose a new top-down approach which is based on disaggregating the top-level forecasts according to forecasted rather than the conventional historical (and static) proportions. We apply the two new approaches, where we forecast tourism demand for Australia and the states from both hierarchies. Our forecasts show a decline in the aggregate domestic tourism demand for Australia over the two years This decline is mainly driven by a decline in tourism demand in the states of New South Wales and Victoria.

Hierarchical time series
Alternative approaches to hierarchical forecasting
The bottom-up approach
Top-down approaches based on historical proportions
Top-down approach based on forecasted proportions
The optimal combination approach
Prediction intervals
Forecasting individual series
Forecast performance evaluation
Forecasts
Forecasts for Australia and the states
Further forecasts from hierarchy 1
Further forecasts from hierarchy 2
Findings
Summary and conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.