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...
- 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