Statistical Models for Forecasting Tourists’ Arrival in Kenya

Autor:	Lawrence Areba Bichanga, Albert Orwa Akuno, Michael Oduor Otieno, Charles Wambugu Mwangi
Rok vydání:	2015
Předmět:	Operations research Service delivery framework Order (exchange) Moving average Computer science Section (archaeology) Exponential smoothing Common knowledge Econometrics Statistical model Autoregressive integrated moving average
Zdroj:	Open Journal of Statistics. :60-65
ISSN:	2161-7198 2161-718X
DOI:	10.4236/ojs.2015.51008
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f98128085c2a0058cac13560e02baad5 https://doi.org/10.4236/ojs.2015.51008 Zobrazit plný text záznamu