Predictive Time Series Models for Tourism Demand in Peru

Alcántara-Moreno O.R.; Sánchez-Ticona R.J.; Tenorio-Cabrera J.L.; Torres-Villanueva M.; Gómez-Avila J.A.; Santos-Fernández J.P. · CISCI 2023 - Vigesima Segunda Conferencia Iberoamericana en Sistemas, Cibernetica e Informatica, Vigesimo Simposium Iberoamericano en Educacion, Cibernetica e Informatica, SIECI 2023 - Memorias (2023) · DOI: 10.54808/cisci2023.01.1

Resumen

The present research aimed to define the most accurate predictive model for tourist demand in Peru through the evaluation of methodologies using time series models. Historical data on the monthly arrival of international tourists in Peru were extracted from the Ministry of Foreign Trade and Tourism (MINCETUR). Three predictive models were modeled and analyzed: regression, Holt-Winters, and ARIMA, for predicting and validating the most accurate predictive model. The statistical software R was used for the statistical analysis. It was confirmed that the linear regression method provides better accuracy in prediction.

Instituciones: Universidad César Vallejo