Statistical Models for Forecasting Tourists’ Arrival in Kenya

Albert Orwa Akuno¹, Michael Oduor Otieno², Charles Wambugu Mwangi¹, Lawrence Areba Bichanga¹

¹Department of Mathematics, Egerton University, Egerton, Kenya.
²Department of Mathematics and Computer Science, Laikipia University, Nyahururu, Kenya.

DOI: 10.4236/ojs.2015.51008                

Abstract

In this paper, an attempt has been made to forecast tourists’ arrival using statistical time series modeling techniques—Double Exponential Smoothing and the Auto-Regressive Integrated Moving Average (ARIMA). It is common knowledge that forecasting is very important in making future decisions such as ordering replenishment for an inventory system or increasing the capacity of the available staff in order to meet expected future service delivery. The methodology used is given in Section 2 and the results, discussion and conclusion are given in Section 3. When the forecasts from these models were validated, Double Exponential Smoothing model performed better than the ARIMA model.

Keywords

Exponential Smoothing, ARIMA Model, Tourists’ Arrival Data

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Satya, P., Ramasubramanian, V. and Menta, S.C. (2007) Statistical Models for Forecasting Milk Production in India. Journal of the Indian Society of Agricultural Statistics, 61, 80-83.
[2]	Padhan, P.C. (2011) Forecasting International Tourists Footfalls in India: An Assortment of Competing Models. International Journal of Business and Management, 6, 190-202.
[3]	Gardener, E.S. (1985) Exponential Smoothing—The State of the Art. Journal of Forecasting, 4, 1-28. http://dx.doi.org/10.1002/for.3980040103
[4]	Jani, P.N. (2014) Business Statistics: Theories and Applications. PHI Learning Private Limited, Delhi.
[5]	Box, G.E.P. and Jenkins, G.M. (1970) Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
[6]	Pankratz, A. (1983) Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. John Wiley and Sons, New York. http://dx.doi.org/10.1002/9780470316566
[7]	Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (1998) Forecasting: Methods and Applications. John Wiley & Sons, New York.