Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/1021
Título: | Combining Genetic Algorithms, Neural Networks and Data Filtering for Time Series Forecasting |
Autor(es): | Neves, José Cortez, Paulo |
Palavras-chave: | Genetic Algoritms Neural Networks Data Filtering Time Series |
Data: | Out-1998 |
Citação: | MASTORAKIS, Nicos E. ed. lit. – “IMACS International Conference on Circuits, Systems and Computers (IMACS-CSC'98)”, 2, Piraeus, Greece, 1998”. ISBN 960-8485-06-1. vol. 2, p. 933-939. |
Resumo(s): | In the last few decades an increasing focus as been put over the field of Time Series Forecasting (TSF), the forecast of a time ordered variable. Contributions from the arenas of Operational Research, Statistics, and Computer Science as lead to solid TSF methods (eg. Exponential Smoothing or Regression) that replaced the old fashion ones, which were primary based on intuition. Although these methods give accurate forecasts on linear Time Series (TS), their handicap is with noise or nonlinear components, which is a commum situation (eg. in financial daily TS). An alternative approach for TSF as recently emerged from the field of Artificial Intelligence, where new optimization algorithms, such as Genetic Algorithms and Artificial Neural Networks have became popular. Following this trend, the present work reports on a Genetic Algoritm Neural Network system, and in its use for TSF. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/1021 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DSI - Engenharia da Programação e dos Sistemas Informáticos |