Neural network approach to forecasting of quasiperiodic financial time series

被引：61
|
作者：
Bodyanskiy, Yevgeniy [1 ]
Popov, Sergiy [1 ]
机构：
[1] Kharkiv Natl Univ Radioelect, Control Syst Res Lab, UA-61166 Kharkov, Ukraine
来源：
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2006年 / 175卷 / 03期
关键词：
time series; forecasting; finance; neural networks; combining of forecasts;
D O I：
10.1016/j.ejor.2005.02.012
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
A novel mural network approach to forecasting of financial time series based on the presentation of the series as a combination of quasiperiodic components is presented. Separate components may have aliquant, and possibly non-stationary frequencies. All their parameters are estimated in real time in an ensemble of predictors, whose outputs are then optimally combined to obtain the final forecast. Special architecture of artificial neural network and learning algorithms implementing this approach are developed. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码：1357 / 1366
页数：10
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