A comparison of exponential smoothing and neural networks in time series prediction

In this article, we compare the accuracy of the forecasts for the exponential smoothing (ES) approach and the radial basis function neural networks (RBFNN) when three nonlinear time series with trend and seasonal cycle are forecasted. In addition, we consider the recommendations of preprocessing by...

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Autores:
Velásquez Henao, Juan David
Zambrano Pérez, Cristian Olmedo
Franco Cardona, Carlos Jaime
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/74156
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/74156
http://bdigital.unal.edu.co/38633/
Palabra clave:
Forecasts combination
nonlinear models
artifi cial neural networks
nonlinear time series
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional