Forecasting U.S. Tourist Arrivals using Singular Spectrum

5 This paper introduces Singular Spectrum Analysis (SSA) for tourism demand forecasting 6 via an application into total monthly U.S. Tourist arrivals from 1996-2012. The global 7 tourism industry is today, a key driver of foreign exchange inflows to an economy. Here, we 8 compare the forecasting results from SSA with those from ARIMA, Exponential Smoothing 9 (ETS) and Neural Networks (NN). We find statistically significant evidence proving that 10 the SSA model outperforms the optimal ARIMA, ETS and NN models at forecasting total 11 U.S. Tourist arrivals. The study also finds SSA outperforming ARIMA at forecasting U.S. 12 Tourist arrivals by country of origin with statistically significant results. In the process, we 13 find strong evidence to justify the discontinuation of employing ARIMA, ETS and a feed14 forward NN model with one hidden layer as a forecasting technique for U.S. Tourist arrivals 15 in the future, and introduce SSA as its highly lucrative replacement. 16