Modelling and Forecasting Tourism Demand for Sri Lanka

Abstract

Tourism makes a substantial contribution to Sri Lanka‟s economy through generation of employment opportunities and foreign exchange earnings. During the past thirty years up to 2009 Sri Lanka‟s tourism has had many obstacles mainly due to the unstable security situation that prevailed. Additionally, the Tsunami disaster in 2004 and the world economic crisis started in 2008 also had adverse effects on the industry. At present, these holdups have either been resolved or absent. The improved political and security situation coupled with indefatigable efforts for post-war economic and infrastructure development has increased the attraction of international tourists towards Sri Lanka. Currently the industry has re-entered to a growth track and is poised to reach its full potential as a safe tourism destination. Accurate forecasts of tourist arrivals are of critical importance to the tourism industry. It would help to ensure the availability of required infrastructure and services when demand materialises. Therefore demand forecasting has become a very interesting topic in tourism research. This study aims to forecast tourist arrivals to Sri Lanka by using quantitative methods. It qualifies past information about a phenomenon by applying mathematical rules which take advantage of the underlying patterns and relationships in the data. To achieve this, monthly tourist arrival data from January, 2010 to October, 2014 are used to build models and evaluate the forecasting performance. The reason for selection and confinement to this duration is unique growth trend observed in tourist arrivals after conclusion of civil war in May 2009, and the absence of factors which contributed to the downward trend before 2009. From the analysis it appears that forecasts from Holt Winters Multiplicative Seasonal Model always outperforms those from Holt-Winters Linear Exponential Smoothing Model, Multiple Regression Model, Vector Auto Regression Model, SARIMA Model and Neural Network Models (i.e. Elman, Feedforward Backpropagation and NARX) in terms of out-of-sample performance used (for Total tourist arrivals, India, United Kingdom and Maldives) and most of the accuracy measures. But for France, SARIMA model generated the most accurate forecasts and for China and Germany, Holt Winters Additive Seasonal Method showed more accurate forecasts. Forecasting accuracy of the neural networks was relatively low due to the limitations of the available data set. But when such limitations are removed the use of neural network approach for forecasting may yield better results because neural networks are capable of representing knowledge based on massive parallel processing and pattern recognition based on past experience and are expected to be superior to statistical methods in forecasting. When the availability of data increases, forecasting accuracy will be higher. Since the ultimate goal of this study is to support information needs of stakeholders in Sri Lankan tourism industry by providing information about future flow of the tourist arrivals, a prototype web-based system was implemented. It provides forecasts for total tourist arrivals and six country-specific arrivals. The system also includes experts‟ opinions (as articles) about those forecasts in order to make such forecasts more reliable via judgmental adjustments.