Automatically Recognizing Stock Patterns Using RPCL Neural Networks
- 10.2991/iske.2007.28How to use a DOI?
- Competitive learning, feed-forward neural network, pattern analysis, self-organizing map, time series analysis.
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
- © 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/).
Cite this article
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 -