The exponential smoothing in the forecast of non-stational series.

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Publicación 7. Vol 4. No 1. (Español (España)) HTML (Español (España)) EPUB (Español (España)) XML (Español (España))
Published: Jan 4, 2020
DOI: https://doi.org/10.33262/cienciadigital.v4i1.1073
Keywords:
Time series, exponential smoothing, trend, Seasonality, tourism.

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Mercedes del Carmen Franco Rodríguez
University of Havana
Efraín Velasteguí López
Editorial Digital Science

Abstract

Tourism is one of the most dynamic sectors of the Cuban economy and one of its main sources of income. As of May 2019, the arrival of visitors to Cuba has begun to slow down as a result of the resurgence of the blockade. The immediate effects of the enactment of Title III of the Helms Burton Act have caused a slowdown in the growth in the number of visitors and income in recent months. In statistical terms, these effects translate into a change of trend in the series of visitors that are also characterized by their marked seasonality. The study addresses econometric techniques for the seasonally adjusted series and the search for short-term forecasts that guarantee the minimum error. Exponential Smoothing is used specifically Holt-Winters Multiplicative Model to project the behavior of the arrival of visitors. The forecasts of the series are an important tool for the decision makers in the sector that face a dynamic and highly uncertain environment.

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How to Cite
Franco Rodríguez, M. del C., & Velasteguí López, E. (2020). The exponential smoothing in the forecast of non-stational series. Ciencia Digital, 4(1), 97-110. https://doi.org/10.33262/cienciadigital.v4i1.1073
Issue
Vol. 4 No. 1 (2020): Educación e igualdad de género
Section
Artículos
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biography

Mercedes del Carmen Franco Rodríguez, University of Havana

Doctora en Ciencias Económicas. Profesora Titular de la Facultad de Turismo de la Universidad de la Habana

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