Abstract

The paper deals with forecasting international tourists footfalls in India, applying an assortment of uni-variate
time series forecasting models, for monthly data spreading over Dec 1990 to Jan 2010. The forecasting
performance of various competing models is evaluated with MAPE and other criteria. Also the actual and
forecasted values are compared since Feb 2010 to Sept 2010 for better evaluation. The SARIMA model performs
better than other competing model for forecasting, with lowest MAPE value. In fact it has an advantage over
other models as it explicates autoregressive and moving average process not only for the data series but also of
seasonality. As a policy implication, SARIMA model can be used for forecasting tourists demand in India.

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