Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method
提出一种基于数据重组的多序列结构时间序列方法，仅需单一变量即可捕捉序列间依赖关系，用于季节性旅游需求预测。在香港入境旅游数据中，该方法比传统单变量模型预测更准确。
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.