Abstract:
Tourism contributes significantly to the economic growth of many countries
and regions. Considering the rapid increase in international tourism demand
over the last few decades, accurate predictions of future trends of tourism
demand are of particular importance to both tourism policymakers and
tourism business practitioners. In most destinations, tourism demand displays
significant seasonal variations. Seasonality affects tourism in various ways and
is responsible for difficulties in gaining access to capital, high risks of
investment and business failures, the ineffective utilization of resources and
facilities, and difficulties in maintaining a consistent service quality. The main
purpose of this study was to formulate a suitable model to forecast future
tourist arrivals considering the seasonal variations using time series approach.
This study has found a time series model to forecast future tourist arrivals by
considering the data from January 2010 to December 2018.Data were obtained
from the series of Annual Statistical Reports published by the Sri Lanka
Tourism Development Authority. According to the behavior of Auto Correlation
Function, Partial Auto Correlation Function of differencing data and with the
results of Augmented Dickey Fuller test, several hypothesized parsimonious
seasonal ARIMA (p, d, q) models were checked. The Schwarz Information
Criterion (SIC), and the Akaike Information Criterions (AIC) are used to
determine to select the best fitted model for the data. ARIMA (0, 1, 1) (1, 1, 0)
has been selected as fitted seasonal model for the forecasting purposes. A
comprehensive knowledge of seasonal patterns of tourism demand and the
accurate prediction of their future values will contribute to effective planning
and operations management, such as staffing, resource allocation and capacity
management, etc.
