Seasonality in Tourism and Forecasting Foreign Tourist Arrivals in India

Abstract

In the present age of globalization, technology-revolution and sustainable development, the presence of seasonality in tourist arrivals is considered as a key policy issue that affects the global tourism industry by creating instability in the demand and revenues. The seasonal component in a time-series distorts the prediction attempts for policy-making. In this context, it is quintessential to suggest an accurate method of producing the reliable forecast of foreign tourist arrivals. This paper evaluated the performance of Holt-Winters' and Seasonal ARIMA models for forecasting foreign tourist arrivals in India. The data on India's inbound tourism from Jan-2001 to June-2018 were used for preparing the forecast for the period July-2018 to June-2020. On the basis of Mean Absolute Error, Mean Absolute Percentage Error and Mean Square Error, the findings infer the relative efficiency of Holt-Winters' model over Seasonal ARIMA model in forecasting the foreign tourist arrivals in India. Thus, to reduce the perceived negative impacts of seasonality in Indian inbound tourism and to ensure foreign tourist visits round the year, niche products best suitable for Indian climatic and socio-cultural-institutional conditions need to be introduced and promoted in a large scale both at the national and global levels.