Automatically Recognizing Stock Patterns Using RPCL Neural Networks

DOI

10.2991/iske.2007.28How to use a DOI?

Keywords

Competitive learning, feed-forward neural network, pattern analysis, self-organizing map, time series analysis.

Abstract

Stock patterns are those that occur frequently in stock time series, containing valuable forecasting information. In this paper, an approach to extract patterns and features from stock price time series is introduced. Thereafter, we employ two ANN-based methods to conduct clustering analyses upon the extracted samples, which are the self-organizing map (SOM) and the competitive learning. Besides, we introduce an improved version of the rival penalized competitive learning (RPCL), and furthermore conduct a comparative study between the clustering performances of the improved RPCL and the SOM. Experimental results show that a better clustering performance can be achieved by the former

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xinyu Guo
PY  - 2007/10
DA  - 2007/10
TI  - Automatically Recognizing Stock Patterns Using RPCL Neural Networks
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 157
EP  - 164
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.28
DO  - 10.2991/iske.2007.28
ID  - Guo2007/10
ER  -