Evolving time series forecasting ARMA models

被引：65

作者：

Cortez, P

Rocha, M

Neves, J

机构：

[1] Univ Minho, Dept Sistemas Informacao, P-4800058 Guimaraes, Portugal

[2] Univ Minho, Dept Informat, P-4710057 Braga, Portugal

来源：

JOURNAL OF HEURISTICS | 2004年 / 10卷 / 04期

关键词：

ARMA models; evolutionary algorithms; bayesian information criterion; model selection; time series analysis;

D O I：

10.1023/B:HEUR.0000034714.09838.1e

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Time Series Forecasting (TSF) allows the modeling of complex systems as "black-boxes", being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.

引用

页码：415 / 429

页数：15

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