A Framework of Predictive Analysis of Tourist Inflow in the Beaches of West Bengal: A Study of Digha-Mandarmoni Beach
In Proceedings of the First International Conference on Computational Intelligence, Communication, and Business Analytics (CICBA 2017), March 24-25, 2017, Kolkata, INDIA. Publisher: Springer-Verlag, CCIS Series., Forthcoming
15 Pages Posted: 20 Mar 2017
Date Written: March 11, 2017
Tourism is increasingly becoming an extremely important sector with its rapidly increasing contribution to GDP of any state or country as a whole. Analyzing and predicting tourist inflow not only enables us to make an accurate estimate of the number of tourists that is likely to visit a destination, but it also provides us with an opportunity to gear up the capacities of that place in terms of logistics, hospitality etc. in order to cater to the tourists leading to an overall socioeconomic development of the place. This paper presents a study on the tourism demand for two very popular beaches of the state of West Bengal in India. In this work, time series values of the domestic tourist inflow to Digha and Mandarmoni beaches in West Bengal are used for the period of January 2008-December 2014. The time series is decomposed into its components – trend, seasonal, and random – in order to make further analysis. Based on the structural analysis, five different approaches of forecasting are formulated and the forecast accuracy is computed for each of the methods. Using R statistical tool, extensive results have been presented that provide very meaningful insights to the tourists’ inflow time series. The results also demonstrate the effectiveness of our proposed forecasting framework.
Keywords: Forecasting, Tourist inflow, Holt-Winters forecasting model, ARIMA, Time series decomposition, Trend, Seasonality, R programming language
JEL Classification: C22; C32; C53; E17; E37; L83
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