Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm

被引：43

作者：

Mammadli, Sadig ^{[1
,2
]}

机构：

[1] Odlar Yurdu Univ, Dept Bussines Econ & Management, AZ-1008 Baku, Azerbaijan

[2] Univ Kyrenia, Dept Banking & Finance, Kyrenia, North Cyprus, Turkey

来源：

9TH INTERNATIONAL CONFERENCE ON THEORY AND APPLICATION OF SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTION, ICSCCW 2017 | 2017年 / 120卷

关键词：

Financial time series; neural network; prediction; optimization; Levenberg-Marquardt;

D O I：

10.1016/j.procs.2017.11.285

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper discusses the applications of Artificial Neural Networks (ANN) using Levenberg-Marquardt optimization algorithm for prediction of financial time series. ANN based on Levenberg-Marquardt training algorithm outperforms gradient decent, conjugate gradient and other algorithms that use the first order derivative of performance index to optimize ANN weights. Levenberg-Marquardt algorithm uses a second order derivative of performance index (curvature information on error surface) as a Guassi-Newton algorithm, but it approximate Hessian matrix by the Jacobian (gradient). Experimental results shows efficiency using ANN based on Levenberg-Marquardt algorithm (c) 2018 The Authors. Published by Elsevier B.V.

引用

页码：602 / 607

页数：6

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