Combining Artificial Neural Network and Particle Swarm System for Time Series Forecasting

被引：0

作者：

Neto, Paulo S. G. de M. ^{[1
]}

Petry, Gustavo G. ^{[1
]}

Aranildo Rodrigues, L. J. ^{[2
]}

Ferreira, Tiago A. E. ^{[2
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, BR-50732970 Recife, PE, Brazil

[2] Univ Fed Rural Pernambuco, Dept Stat & Inforamt, BR-52171900 Recife, PE, Brazil

来源：

IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6 | 2009年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Forecasting systems have been widely used for decision making and one of its most promising approaches is based on Artificial Neural Networks (ANN). In this paper, a hybrid swarm system is presented for the time series forecasting problem, which consists of an intelligent hybrid model composed of an ANN combined with Particle Swarm Optimizer (PSO). The proposed method searches the relevant time lags for a correct characterization of the time series, as well as the number of processing units in the hidden layer, the training algorithm and the modeling of ANN. The proposed method shows an efficient procedure to adjust the ANN parameters through the use of a particle swarm optimization mechanism. An experimental analysis is conducted with the proposed method using six real world time series and the results are discussed according to five performance measures.

引用

页码：2417 / +

页数：2

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