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Volume 26 Issue 4
Apr.  2004
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Luo Jirun, Xu Chenghe, Zhang Shichang, Hong Wenjie. ANALYSIS OF BEAM-WAVE INTERACTION IN THE COUPLED DOUBLE-CAVITY GYROTRON[J]. Journal of Electronics & Information Technology, 1987, 9(6): 507-517.
Citation: Wang Xiao-ye, Wang Zheng-ou. Stock Market Time Series Data Mining Based on Regularized Neural Network and Rough Set[J]. Journal of Electronics & Information Technology, 2004, 26(4): 625-631.

Stock Market Time Series Data Mining Based on Regularized Neural Network and Rough Set

  • Received Date: 2002-11-13
  • Rev Recd Date: 2003-04-21
  • Publish Date: 2004-04-19
  • This paper presents a new method of stock market time series data mining. It combines regularized neural network with rough set. The process includes preprocessing of time series and data mining. The preprocessing cleans and filters time series. Then, the time series are partitionel into a series of static patterns, which is based on the trend (i.e., increasing or decreasing) of closing price. The most important predicting attributes identified from every model form an information table. The regularized neural network is used to learn and predict the data. Rough set can extract rule knowledge in the neural network, which can be used to predict the time series behavior in the future. This method combines the generalization faculty of regularized neural network and the rule reduction capability of rough set. The experimental results demonstrate the effectiveness of the algorithm.
  • Das G, Gunopulos D. Finding similar time series. In Proc. of the Conference on Principles of Knowledge Discovery and Data Mining, Trondheim, Norway, 1997: 124-135.[2]Das G, Lin K, Mannila H, Renganathan G, Smyth P. Rule discovery from time series. In Proc. of the 4th Int. Conference on Knowledge Discovery and Data Mining, New York, NY, Aug. 27-31,1998: 179-183.[3]Hansen V J, Nelson R D. Data mining of time series using stacked generalizers[J].Neurocomputing.2002, 43(1):173-184[4]Last M, Klein Y. Knowledge discovery in time series databases[J].IEEE Trans. on System, Man and Cybernetics-part B.2001, 31(1):160-169[5]Qin Zh, Mao Z. A new algorithm for neural network architecture study. In Proc. of the 3rd World Congress on Intelligent Control and Automation, Hefei, China. June 2000: 795-799.[6]Lingras P. Comparison of ne0fuzzy and rough neural networks. Information Sciences, 1998, (110):207-215.[7]Girosi F, Jones M. Regularization theory and neural networks architectures[J].Neural Computation.1995, 7(2):219-269[8]王国胤.Rough集理论与知识获取.西安:西安交通大学出版社,2001:99-104.[9]Jiawei H, Micheline K. Data Mining: Concepts and Techniques. San Meteo, CA: Morgan Kaufmann Publishers, Inc., 2001, Chapter 5.[10]Reed R. Pruning algorithms-a survey[J].IEEE Trans. on Neural Networks.1993, 4(5):740-747[11]李冬梅,王正欧.提高前向神经网络泛化性能和实时性能的新方法.电机与控制学报,2002,6(3):241-244.[12]常犁云,王国胤等.一种基于Rough Set理论的属性约简及规则提取方法.软件学报,1999,10(11):1206-1211.[13]宋擒豹,沈钧毅.神经网络数据挖掘方法中的数据准备问题.计算机工程与应用,2000,36(12):102-104.
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