Adaptive neural network model for time-series forecasting

被引：66

作者：

Wong, W. K. ^{[1
]}

Xia, Min ^{[1
,2
]}

Chu, W. C. ^{[1
]}

机构：

[1] Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China

[2] Donghua Univ, Coll Informat Sci & Technol, Shanghai, Peoples R China

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2010年 / 207卷 / 02期

关键词：

Time-series; Forecasting; Adaptive metrics; Neural networks; PREDICTION;

D O I：

10.1016/j.ejor.2010.05.022

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this study, a novel adaptive neural network (ADNN) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time-series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust forecasting results by the relative error and make them more accurate. The proposed ADNN method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the auto-regression (AR), artificial neural network (ANN), and adaptive k-nearest neighbors (AKN) models. The ADNN model is proved to benefit from the merits of the ANN and the AKN through its' novel structure with high robustness particularly for both chaotic and real time-series predictions. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：807 / 816

页数：10