%0 Journal Article
%T Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method
%A Doris Chenguang Wu
%A Gang Li
%A Jason Li Chen
%A Shujie Shen
%J Journal of Travel Research
%@ 1552-6763
%D 2019
%R 10.1177/0047287517737191
%X Multivariate forecasting methods are intuitively appealing since they are able to capture the interseries dependencies, and therefore may forecast more accurately. This study proposes a multiseries structural time series method based on a novel data restacking technique as an alternative approach to seasonal tourism demand forecasting. The proposed approach is analogous to the multivariate method but only requires one variable. In this study, a quarterly tourism demand series is split into four component series, each component representing the demand in a particular quarter of each year; the component series are then restacked to build a multiseries structural time series model. Empirical evidence from Hong Kong inbound tourism demand forecasting shows that the newly proposed approach improves the forecast accuracy, compared with traditional univariate models
%K multivariate
%K structural time series model
%K seasonality
%K tourism demand
%K forecasting
%U https://journals.sagepub.com/doi/full/10.1177/0047287517737191