Artificial neural networks for non-stationary time series

被引：70

作者：

Kim, TY

Oh, KJ ^{[1
]}

Kim, CH

Do, JD

机构：

[1] Hansung Univ, Div Business Adm, Seoul, South Korea

[2] Keimyung Univ, Dept Stat, Taegu, South Korea

[3] Korea Deposit Insurance Corp, Seoul, South Korea

来源：

NEUROCOMPUTING | 2004年 / 61卷

基金：

新加坡国家研究基金会;

关键词：

non-stationary time series; overfitting; artificial neural networks; asymptotic stationary; autoregressive model;

D O I：

10.1016/j.neucom.2004.04.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The use of artificial neural networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：439 / 447

页数：9

共 9 条

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Anderson T. W., 1971, STAT ANAL TIME SERIE

[2]

[Anonymous], 1996, NEURAL NETWORKS FINA

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