金融时间序列模糊边界预测研究

传统的金融时间序列预测方法以精确的输入数据为研究对象,在建立回归模型的基础上做单步或多步预测,预测结果是一个或多个具体的值.由于金融市场的复杂性,传统的预测方法可靠度较低.提出将金融时间序列模糊信息粒化成一个模糊粒子序列,运用支持向量机对模糊粒子的上下界进行回归,然后应用回归所得到的模型分别对上下界进行单步预测,从而将预测的结果限定在一个范围之内.这是一种全新的思路.以上证指数周收盘指数为实验数据,实验结果表明了这种方法的有效性.

Research on Forccasting the Fuzzy Boundary of Financial Time Series

The traditional financial time series forecasting methods use accurate input data for the object of study,and then make single-step or multi-step prediction based on the established regression model.So its prediction result is one or more specific values.But because of the complexity of financial markets,the traditional forecasting methods are less reliable.In this paper,we transform the financial time series into fuzzy grain particle sequences,and use support vector machine regression to regress the upper and lower bounds of the fuzzy particles,and then apply regression model single-step prediction on the upper and lower bounds,which will limit the predict results within a range.This is a new idea.The Shanghai Composite Index Week closing index for the experimental data,experimental results show the effectiveness of this approach.