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Statistical Models for Forecasting Tourists’ Arrival in Kenya

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dc.contributor.author Albert Orwa Akuno, Michael Oduor Otieno, Charles Wambugu Mwangi, Lawrence Areba Bichanga
dc.date.accessioned 2019-10-22T12:53:33Z
dc.date.available 2019-10-22T12:53:33Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/123456789/9379
dc.description.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. en_US
dc.title Statistical Models for Forecasting Tourists’ Arrival in Kenya en_US


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