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
Summary
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.
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