title: |
An Innovative Use of Historical Data for Neural Network Based Stock Prediction |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.153 (how to use a DOI) | |
author(s): |
Tak-chung Fu, Tsz-leung Cheung, Fu-lai Chung, Chak-man Ng |
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corresponding author: |
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publication date: |
October 2006 |
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keywords: |
prediction, stock time series, artificial neural network, time point selection |
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abstract: |
Using artificial neural network is a common approach for the stock time series prediction problem. Unlike variety of researches that focus on selecting different indicators, network training, network architecture, etc., we are focusing on the selection of appropriate time points from the time sequence to serve as the input of the neural network prediction system for dimensionality reduction. We propose to select the time points based on data point importance using perceptually important point identification process. The empirical result shows that the proposed method generally outperformed the traditional method using uniform time delay. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |