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Forecasting Seasonal Tourism Demand Using a Multi-Series Structural Time Series Method

Chen, Li, Li, Gang, Wu, DC and Shen, S (2017) Forecasting Seasonal Tourism Demand Using a Multi-Series Structural Time Series Method Journal of Travel Research.

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Abstract

Multivariate forecasting methods are intuitively appealing since they are able to capture the inter-series dependencies, and therefore may forecast more accurately. This study proposes a multi-series 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 multi-series 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.

Item Type: Article
Subjects : Hospitality & Tourism
Divisions : Faculty of Arts and Social Sciences > School of Hospitality and Tourism Management
Authors :
NameEmailORCID
Chen, Lil.chen@surrey.ac.ukUNSPECIFIED
Li, GangG.Li@surrey.ac.ukUNSPECIFIED
Wu, DCUNSPECIFIEDUNSPECIFIED
Shen, SUNSPECIFIEDUNSPECIFIED
Date : 9 August 2017
Copyright Disclaimer : Copyright © 2017 Sage Publications. Reprinted by permission of SAGE Publications.
Uncontrolled Keywords : multivariate, structural time series model, seasonality, tourism demand, forecasting
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 09 May 2017 11:56
Last Modified : 19 Jul 2017 14:07
URI: http://epubs.surrey.ac.uk/id/eprint/814129

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