Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network

Rozaida Ghazali; Abir Jaafar Hussain; Nazri Mohd Nawi; Baharuddin Mohamad

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Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network

机译：动态脊线多项式神经网络对金融时间序列的平稳预测

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摘要

This research focuses on using various higher order neural networks (HONNs) to predict the upcoming trends of financial signals. Two HONNs models: the Pi-Sigma neural network and the ridge polynomial neural network were used. Furthermore, a novel HONN architecture which combines the properties of both higher order and recurrent neural network was constructed, and is called dynamic ridge polynomial neural network (DRPNN). Extensive simulations for the prediction of one and five steps ahead of financial signals were performed. Simulation results indicate that DRPNN in most cases demonstrated advantages in capturing chaotic movement in the signals with an improvement in the profit return and rapid convergence over other network models.

机译：这项研究的重点是使用各种高阶神经网络（HONN）来预测金融信号的未来趋势。使用了两个HONNs模型：Pi-Sigma神经网络和岭多项式神经网络。此外，构造了一种结合了高阶和递归神经网络特性的新型HONN架构，称为动态脊多项式神经网络（DRPNN）。进行了广泛的模拟，以预测财务信号之前的一到五步。仿真结果表明，在大多数情况下，DRPNN在捕获信号中的混沌运动方面具有优势，与其他网络模型相比，它具有更高的利润回报率和更快的收敛性。

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来源
《Neurocomputing》 |2009年第12期|2359-2367|共9页
作者
Rozaida Ghazali; Abir Jaafar Hussain; Nazri Mohd Nawi; Baharuddin Mohamad;
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作者单位

Information Technology and Multimedia Faculty, Universiti Tun Hussein Onn, Malaysia;

School of Computing and Mathematical Sciences, Liverpool John Moores University, UK;

Information Technology and Multimedia Faculty, Universiti Tun Hussein Onn, Malaysia;

Information Technology and Multimedia Faculty, Universiti Tun Hussein Onn, Malaysia;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
dynamic ridge polynomial neural network; financial time series; higher order neural networks; pi-sigma neural networks; ridge polynomial neural networks;

机译：动态岭多项式神经网络财务时间序列;高阶神经网络pi-sigma神经网络;岭多项式神经网络;

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专利

1. Dynamic Ridge Polynomial Neural Networks for multi-step financial time-series prediction [J] . Abir Jaafar Hussain, Rozaida Ghazali, Dhiya Al-Jumeily International Journal of Intelligent Systems Technologies and Applications . 2008,第1a2期

机译：动态岭多项式神经网络用于多步财务时间序列预测
2. Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals [J] . Rozaida Ghazali, Abir Jaafar Hussain, Panos Liatsis Expert Systems with Application . 2011,第4期

机译：动态岭多项式神经网络：预测单变量非平稳和平稳交易信号
3. The application of ridge polynomial neural network to multi-step ahead financial time series prediction [J] . R. Ghazali, A. J. Hussain, P. Liatsis, Neural computing & applications . 2008,第3期

机译：岭多项式神经网络在多步提前财务时间序列预测中的应用
4. Dynamic Ridge Polynomial Neural Network for Financial Time Series Prediction [C] . Abir Jaafar Hussain, Rozaida Ghazali, Dhiya Al-Jumeily, Innovations in Information Technology . 2006

机译：用于金融时序序列预测的动态脊多项式神经网络
5. Automatically Defined Templates for Improved Prediction of Non-stationary, Nonlinear Time Series in Genetic Programming. [D] . Moskowitz, David. 2016

机译：自动定义的模板，用于改进遗传编程中非平稳，非线性时间序列的预测。
6. Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks [O] . Dhiya Al-Jumeily, Rozaida Ghazali, Abir Hussain -1

机译：使用动态岭多项式神经网络预测物理时间序列
7. Predicting physical time series using dynamic ridge polynomial neural networks. [O] . Dhiya Al-Jumeily, Rozaida Ghazali, Abir Hussain 2014

机译：使用动态岭多项式神经网络预测物理时间序列。
8. Tests for Non-Linear Dynamics in Systems of Non-Stationary Economic Time Series:The Case of Short-Term US Interest Rates [R] . Jones, B. E., Nesmith, T. D. 2000

机译：非平稳经济时间序列系统中的非线性动力学测试：美国短期利率的案例

Non-stationary and stationary prediction of financial time series using dynamic ridge polynomial neural network

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