Investigating Neural Network Modeling Decisions for the Australian All-Ordinaries Index

机译：调查澳大利亚普通股指数的神经网络建模决策

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Estimating stock market output depends mainly on identifying nonlinear relationships of input variables. To forecast such systems a non-linear modeling tool is required. This paper describes the experimental approaches for developing an Artificial Neural Network for the purpose of modeling the Australian All Ordinaries Index movement over a prediction horizon of 1 year. Network parameters such as network architectures, input data sizing and periodicity are considered in the development of the network. The evaluation criterion for the Neural Network output is the R Square Statistic.

机译：估计股市产出主要取决于识别输入变量的非线性关系。为了预测这样的系统，需要非线性建模工具。本文介绍了开发人工神经网络的实验方法，目的是对1年预测期内澳大利亚所有普通股指数的运动进行建模。在网络的开发中考虑了网络参数，例如网络体系结构，输入数据大小和周期性。神经网络输出的评估标准是R平方统计量。

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《International Conference on Computational Science - ICCS 2003 Pt.4 Jun 2-4, 2003 Melbourne, Australia and St. Petersburg, Russia》|2003年|p.930-939|共10页
会议地点 Melbourne(AU) St. Petersburg(RU);Melbourne(AU) St. Petersburg(RU)
作者
Andrew Flitman; Mark Barnes; Deniss Teng Tai Kiat;
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作者单位

Monash University, Business Systems, Clayton, Victoria, Australia;

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原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
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Investigating Neural Network Modeling Decisions for the Australian All-Ordinaries Index

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