Publicação
A Meta-Genetic Algorithm for Time Series Forecasting
| Resumo: | Alternative approaches for Time Series Forecasting (TSF) emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Genetic Algorithms (GAs) are popular. The present work reports on a two-level architecture, where a (meta-level) binary GA will search for the best TSF model, being the parameters optimized by a (low-level) GA, which encodes real values. The machine's performance of this approach was compared with conventional forecasting methods, exhibiting good results, specially when trended and nonlinear series are considered. |
|---|---|
| Autores principais: | Cortez, Paulo |
| Outros Autores: | Rocha, Miguel; Neves, José |
| Assunto: | ARMA Models (Meta-)Genetic Algorithms Model Selection Time Series Forecasting |
| Ano: | 2001 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Alternative approaches for Time Series Forecasting (TSF) emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Genetic Algorithms (GAs) are popular. The present work reports on a two-level architecture, where a (meta-level) binary GA will search for the best TSF model, being the parameters optimized by a (low-level) GA, which encodes real values. The machine's performance of this approach was compared with conventional forecasting methods, exhibiting good results, specially when trended and nonlinear series are considered. |
|---|