基于信息粒的模糊时间序列预测模型

Fuzzy Time Series Forecasting Model Based on Information Granule

时间窗口的分割长度是影响预测结果准确性的重要指标之一,因此根据合理粒化将时间序列分割成一些可处理有意义的信息粒,从而得到更有效的非一致划分的分割方法.进一步,提出基于信息粒的模糊时间序列预测模型去预测股指时间序列.模型首先根据信息粒获取时间序列时间窗口的分割;然后在其基础上定义模糊集并将历史序列模糊化;构造模糊逻辑关系并为每一个模糊趋势指派权重;最终根据得到的信息实施预测.实验结果表明,提出的模型具有较高的准确性.

The length of intervals in the universe of discourse is important due to the fact that it can affect the forecasting accuracy. In this paper, information granulation is applied to transform raw time series into meaningful and interpretable granules, and the more effective non-uniform partitioning method for fuzzy time series forecasting is presented.A new fuzzy time series forecasting model is proposed to predict stock index time series.The model first determines the intervals based on information granules,and then define the fuzzy sets and fuzzify the historical data. Third, construct fuzzy relationships and assign weights to each period. Finally, the phase of forecasting is implemented. The experimental results show the proposed model yields higher average forecasting accuracy rates than the existing models.