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Forecasting U.S. tourist arrivals using optimal Singular Spectrum Analysis

Hassani, Hossein, Webster, Allan, Silva, Emmanuel Sirimal and Heravi, Saeed 2015. Forecasting U.S. tourist arrivals using optimal Singular Spectrum Analysis. Tourism Management 46 , pp. 322-335. 10.1016/j.tourman.2014.07.004

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Abstract

This study examines the potential advantages of using Singular Spectrum Analysis (SSA) for forecasting tourism demand. To do this it examines the performance of SSA forecasts using monthly data for tourist arrivals into the Unites States over the period 1996 to 2012. The SSA forecasts are compared to those from a range of other forecasting approaches previously used to forecast tourism demand. These include ARIMA, exponential smoothing and neural networks. The results presented show that the SSA approach produces forecasts which perform (statistically) significantly better than the alternative methods in forecasting total tourist arrivals into the U.S. Forecasts using the SSA approach are also shown to offer a significantly better forecasting performance for arrivals into the U.S. from individual source countries. Of the alternative forecasting approaches exponential smoothing and feed-forward neural networks in particular were found to perform poorly. The key conclusion is that Singular Spectrum Analysis (SSA) offers significant advantages in forecasting tourist arrivals into the US and is worthy of consideration for other forecasting studies of tourism demand.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0261-5177
Date of Acceptance: 2 July 2014
Last Modified: 11 Jan 2021 10:45
URI: http://orca.cardiff.ac.uk/id/eprint/86330

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