摘要
支持向量机(SVM)是数据挖掘中的一项新技术,是借助于最优化方法解决机器学习问题的新工具。在研究了股票数据的特点以及对股票预测的研究结果后,本文根据传统的SVM算法原理,提出一种在线选择训练样本的在线增量训练的方式完成模型更新的动态预测模型(DMDI),使得仅增加较小工作量为代价而获得更高的预测精度成为可能。应用DMDI对股市的大盘和个股的走势分别进行中短期预测,并跟神经网络的预测结果进行了比较。大量数值实验表明,DMDI模型比不进行选择的静态模型和神经网络模型对股票走势的预测更为有效,具有明显的优越性。
Support Vector Machine （ SVM ） which is a new technology used in Data Mining. It is a new tool that accounts for the problems of the Machine Learning by the method of the optimization, Applying the support vector machine method in the research on the non-linear time series economic prediction problem is underway. It is more feasible and predominant than the Neural Networks algorithm in the extending ability and the tallying precision. After we studied the characteristics of the stock data and the rules of the stock market people, we put forward to a dynamic model which bases on the traditional support vector machine arithmetic. The model selects the training data online when we get the new data and then we modify the model each time base on the increased data in the aggregate. It is a dynamic model, so it can catch the real time change of the market. It make the prediction precision be improved comes to truth with the small workload as the cost. In this paper we use the support vector machine and the Time series dynamic model （DMDI） to predict the short-time and the medium-terrn ups and downs in the single stock and the holistic Shanghai stock market. We perform a large numbers of numerical experiments and compared with the results being got based on the methods of the BP neural networks and the static models which is not changed when the new data is got with the time going, and the prediction rightness probability is higher, and it is more feasible in the extending ability and the tallying precision through the actual application. In addition, It can also avoid the difficult problem study of the training data excessively. The results show that the DMDI is more suitable for the forecasting the index time series of the stock market than the BP neural networks and the static models. The model we have proposed in this paper has more advantages in the prediction of the trends of the stock market than the conventional methods.
出处
《重庆师范大学学报（自然科学版）》
CAS
2007年第4期45-49,共5页
Journal of Chongqing Normal University:Natural Science
关键词
支持向量机(SVM)
股票预测
核函数
动态选择
Support Vector Machine （SVM）
stocks prediction
kernel function
dynamic selection
