Statistical Models for Forecasting Tourists’ Arrival in Kenya
Author(s)
Albert Orwa Akuno1,
Michael Oduor Otieno2,
Charles Wambugu Mwangi1,
Lawrence Areba Bichanga1
Affiliation(s)
1 Department of Mathematics, Egerton University, Egerton, Kenya. 2 Department of Mathematics and Computer Science, Laikipia University, Nyahururu, Kenya.
1 Department of Mathematics, Egerton University, Egerton, Kenya. 2 Department of Mathematics and Computer Science, Laikipia University, Nyahururu, Kenya.
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.
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.
Cite this paper
Akuno, A. , Otieno, M. , Mwangi, C. and Bichanga, L. (2015) Statistical Models for Forecasting Tourists’ Arrival in Kenya. Open Journal of Statistics, 5, 60-65. doi: 10.4236/ojs.2015.51008.
Akuno, A. , Otieno, M. , Mwangi, C. and Bichanga, L. (2015) Statistical Models for Forecasting Tourists’ Arrival in Kenya. Open Journal of Statistics, 5, 60-65. doi: 10.4236/ojs.2015.51008.
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.
[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.