Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm
[EN] Time series forecasting is an important tool to support both individual and organizational decisions (e.g. planning production resources). In recent years, a large literature has evolved on the use of evolutionary artificial neural networks (EANN) in many forecasting applications. Evolving neur...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/231956 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/231956 |
| Access Level: | acceso abierto |
| Palabra clave: | Evolutionary computation Genetic algorithms Differential evolution Estimation of distribution algorithm Artificial neural networks Time series Forecasting |
| Sumario: | [EN] Time series forecasting is an important tool to support both individual and organizational decisions (e.g. planning production resources). In recent years, a large literature has evolved on the use of evolutionary artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. In this work, two new approaches of a previous system, automatic design of artificial neural networks (ADANN) applied to forecast time series, are tackled. In ADANN, the automatic process to design artificial neural networks was carried out by a genetic algorithm (GA). This paper evaluates three methods to evolve neural networks architectures, one carried out with genetic algorithm, a second one carried out with differential evolution algorithm (DE) and the last one using estimation of distribution algorithms (EDA). A comparative study among these three methods with a set of referenced time series will be shown. In this paper, we also compare ADANN forecasting ability against a forecasting tool called Forecast Pro(A (R)) (FP) software, using five benchmark time series. The object of this study is to try to improve the final forecasting getting an accurate system. |
|---|