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The authors present and apply the Singular Spectrum Analysis (SSA), a relatively new, non-parametric and data-driven method used for signal extraction (trends, seasonal and business cycle components) and forecasting of the UK tourism income. The results show that SSA outperforms slightly SARIMA and time-varying parameter State Space Models in terms of RMSE, MAE and MAPE forecasting criteria. Signal extraction and forecasting are important aspects in tourism policy making in all countries involved in tourism. A meaningful decomposition of an observed time series in signal and noise components leads to a better understanding of the tourism development process, especially in its relation to macroeconomic environment, and more accurate forecasting of tourism demand.
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