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- W2623776817 abstract "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." @default.
- W2623776817 created "2017-06-15" @default.
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- W2623776817 date "2017-11-08" @default.
- W2623776817 modified "2023-10-11" @default.
- W2623776817 title "Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method" @default.
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- W2623776817 doi "https://doi.org/10.1177/0047287517737191" @default.
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