The Australian Tourism Demand dataset (Wickramasuriya et al. 2019) measures the number of nights Australians spent away from home. It includes 228 monthly observations of Visitor Nights (VNs) from January 1998 to December 2016, and has a cross-sectional grouped structure based on a geographic hierarchy crossed by purpose of travel. The geographic hierarchy comprises 7 states, 27 zones, and 76 regions, for a total of 111 nested geographic divisions. Six of these zones are each formed by a single region, resulting in 105 unique nodes in the hierarchy. The purpose of travel comprises four categories: holiday, visiting friends and relatives, business, and other. To avoid redundancies (Girolimetto et al. 2023), 24 nodes (6 zones are formed by a single region) are not considered, resulting in an unbalanced hierarchy of 525 (304 bottom and 221 upper time series) unique nodes instead of the theoretical 555 with duplicated nodes.
Usage
# 525 time series of the Australian Tourism Demand dataset
vndata
# aggregation matrix
vnaggmatFormat
vndata is a \((228 \times 525)\) ts object, corresponding to
525 time series of the Australian Tourism Demand dataset (1998:01-2016:12).
vnaggmat is the \((221 \times 304)\) aggregation matrix.
References
Girolimetto, D., Athanasopoulos, G., Di Fonzo, T. and Hyndman, R.J. (2024), Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues. International Journal of Forecasting, 40, 3, 1134-1151. doi:10.1016/j.ijforecast.2023.10.003
Wickramasuriya, S.L., Athanasopoulos, G. and Hyndman, R.J. (2019), Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization, Journal of the American Statistical Association, 114, 526, 804-819. doi:10.1080/01621459.2018.1448825