Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods
Accurate time series forecasting are important for displaying the manner in which the past contin- ues to affect the future and for planning our day to day activities. In recent years, a large litera- ture has evolved on the use of computational in- telligence in many forecasting applications. In th...
| Autor principal: | |
|---|---|
| Outros Autores: | , , , |
| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2011
|
| Assuntos: | |
| Texto completo: | http://hdl.handle.net/1822/14840 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/14840 |
| Resumo: | Accurate time series forecasting are important for displaying the manner in which the past contin- ues to affect the future and for planning our day to day activities. In recent years, a large litera- ture has evolved on the use of computational in- telligence in many forecasting applications. In this paper, several computational intelligence techniques (genetic algorithms, neural networks, support vec- tor machine, fuzzy rules) are combined in a distinct way to forecast a set of referenced time series. Fore- casting performance is compared to the a standard and method frequently used in practice. |
|---|