摘要
基于支持向量机的预测模型对上证指数进行预测,并将其预测结果与BP神经网络的预测结果进行对比,其结果表明,支持向量机的预测模型具有较高的拟合和预测精度并优于BP神经网络模型,且支持向量机预测方法计算速度快,准确率高,具有很好的推广应用价值。
This paper predicts Shanghai Stock Exchange (SSE) Composite Index based on the prediction model of support vector maeltine (SVM), compares its predicting result with that of BP neural network, and concludes that the prediction model of SVM is higher in matching and predicting accuracy, and better than BP neural network; in addition, the prediction model of SVM is quicker and higher in calculation and accuracy with better promotional and practical values.
出处
《商业经济》
2011年第3期104-106,共3页
Business & Economy
基金
教育部博士点基金项目(20100091120050)
江苏省人文社科基金项目(10EYC019)
南京大学人文社科项目:基于企业家非理性行为的企业投资行为研究
关键词
支持向量机
上证指数
预测
BP神经网络
support vector machine (SVM), Shanghai Stock Exchange (SSE) Composite Index, prediction, BP neural network