基于模糊时间序列的预测模型——以上证指数为例

Forecasting Model Based on Fuzzy Time Series——With an Example of Shanghai Stock Index

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。文章中首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析,在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合,从结果看新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的预测方法。

Fuzzy set theory was originally developed to handle problems involving human linguistic terms, it can reduce the uncertainty of the complex and highly nonlinear systems, and is widely used in many aspects of our life. In this paper, definition of fuzzy time series is revisited, the exiting fuzzy time series models are analyzed and a new fuzzy time series forecasting method is presented. The Shanghai compound index as the forecasting target, the empirical results show that the proposed method can get a higher forecasting accuracy rate than the exiting on msn, mean error and standard deviation.