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

Authors: Hassani, H., Webster, A., Silva, E.S. and Heravi, S.

Journal: Tourism Management

Volume: 46

Pages: 322-335

eISSN: 0261-5177

DOI: 10.1016/j.tourman.2014.07.004

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. © 2014 Elsevier Ltd.

http://eprints.bournemouth.ac.uk/22822/

Source: Scopus

Forecasting US Tourist arrivals using optimal Singular Spectrum Analysis

Authors: Hassani, H., Webster, A., Silva, E.S. and Heravi, S.

Journal: TOURISM MANAGEMENT

Volume: 46

Pages: 322-335

eISSN: 1879-3193

ISSN: 0261-5177

DOI: 10.1016/j.tourman.2014.07.004

http://eprints.bournemouth.ac.uk/22822/

Source: Web of Science (Lite)

Forecasting U.S. Tourist Arrivals using Singular Spectrum Analysis

Authors: Silva, E.S.

Conference: 34th International Symposium on Forecasting

Dates: 29 June-2 July 2014

Abstract:

This paper introduces Singular Spectrum Analysis (SSA) for tourism demand forecasting via an application into total monthly U.S. Tourist arrivals from 1996-2012. The global tourism industry is today, a key driver of foreign exchange inflows to an economy. We analyse and test the US tourist arrivals data for normality and stationarity initially as both parametric and nonparametric forecasting models are evaluated here. We then forecast and compare the results from SSA with those from ARIMA, Exponential Smoothing (ETS) and Neural Networks (NN). We find statistically significant evidence proving that the SSA model outperforms the optimal ARIMA, ETS and NN models at forecasting total U.S. Tourist arrivals. The study also finds SSA outperforming ARIMA at forecasting U.S. Tourist arrivals by country of origin with statistically significant results. In the process, we find strong evidence to justify the discontinuation of employing ARIMA, ETS and a feedforward NN model with one hidden layer as a forecasting technique for U.S. Tourist arrivals in the future, and introduce SSA as its highly lucrative replacement.

http://eprints.bournemouth.ac.uk/22822/

Source: Manual

Preferred by: Emmanuel Sirimal Silva

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

Authors: Hassani, H., Webster, A., Silva, E.S. and Heravi, S.

Journal: Tourism Management

Volume: 46

Pages: 322-335

ISSN: 0261-5177

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 United 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.

http://eprints.bournemouth.ac.uk/22822/

http://www.journals.elsevier.com/

Source: BURO EPrints