Affiliation:
1. University of Tennessee,
2. North Carolina State University,
Abstract
This study uses three major U.S. national parks as applications of statistically selecting appropriate methods to forecast attendance. Forecasting methods assessed include Naïve 1, Naïve 2, single moving average (SMA), single exponential smoothing (SES), Brown's, Holt's, autoregressive integrated moving average (ARIMA), derived time series cross-section regression (TSCSREG), and time series analysis with explanatory variable models. The mean absolute percentage error (MAPE) is used to measure the accuracy of forecasting methods. Based on the MAPE values, SMA produces the most accurate forecasting, followed closely by ARIMA, Brown's, and Naïve 1 models. Holt's and TSCSREG models produce the next most accurate forecasting, followed by SES, time series analysis with explanatory variable model, and Naïve 2. Methods used in this article are readily transferable to other hospitality and tourism data sets with annual visitation figures. Merits and limits of the proposed forecasting methods are discussed.
Subject
Tourism, Leisure and Hospitality Management,Education
Reference47 articles.
1. Archer, B.H. (1994). Demand forecasting and estimation. In J. R. B. Ritchie & C. R. Goeldner (Eds.), Travel, tourism and hospitality research (4th ed., pp. 105-114). New York: Wiley.
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