Forecast of Chile's tourism demand based on seasonal linear and nonlinear models
DOI:
https://doi.org/10.25145/j.pasos.2021.19.021Keywords:
ARIMA models, tourism demand, short-term estimation, non-linear series, seasonal seriesAbstract
In this paper the emissive and receptive tourism that Chile experienced for the period 2000-2018 has been modeled. Linear regression models with dichotomous variables and ARIMA with seasonal component models have been used to modeling times series and evaluate their forecast errors. The results show that ARIMA with a seasonal component models allow modeling and forecasting of the series that adequately reflect the dynamics of growth and its seasonal behavior with less error in the short term.
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Copyright (c) 2020 Cristian Mauricio Mondaca-Marino, Ailin Arriagada-Millaman
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