Forecasting Ability of Univariate Time Series Approach in Foreign Guest Nights in the Southern Coast of Sri Lanka
Journal Title: International Journal of Research and Review - Year 2018, Vol 5, Issue 8
Abstract
The South Coast of Sri has been an attraction to the tourists for centuries. Even today, the international tourism demand to the region is in the uptrend, resulting high occupancy in the region. The high occupancy increases the demand for accommodation. Hence, the hotel industry should adopt various management practices to maximize profits and optimize operations by accurate forecasting. Therefore, this study was focused on identifying suitable forecasting techniques for occupancy guest nights of international tourism in the South Coast of Sri Lanka. Monthly data of foreign guest nights for the period of January 2008 to December 2016 were obtained from annual reports of 2008 -2016 published by Sri Lanka Tourism Development Authority (SLTDA). Time series plots used for pattern identification. The Decomposition techniques, SARIMA, and Holt-Winters models were tested for forecasting. The Anderson–Darling test, Auto-Correlation Function (ACF), and Ljung-Box Q (LBQ)-test were used to model validation. Forecasting ability of the models was assessed by relative and absolute measurements. Except for ARIMA (1,0,1)(1,2,1)6 all other techniques do not meet the model validation criterion. Therefore, future night occupancy by the foreign guest in the Southern Coast can be forecasted by past night occupancy by foreign guest, past errors and seasonal components. The study concluded that SARIMA performs better than Decomposition and Holt-Winters in forecasting occupancy guest nights. However, the SARIMA model is not capable of capturing the cyclical variations. Therefore, it is recommended to test the Circular Model for de-trended series.
Authors and Affiliations
Konarasinghe Mudiyanselage Udaya Banda Konarasinghe
Keywords
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