模拟时间序列因为在处理数据采集中固有的不确定性和含糊性方面的显著能力而得到了越来越多的的关注，已经有许多模型致力于改进预测准确性和减少预测的计算开销，然而对于预测不确定性的控制、有效的分区间隔和对于不同的分区间隔达到一致的预测准确性方面研究较少。针对现有预测模型的不足，本文提出了一种新的预测模型，新模型增强了预测的性能并允许处理两因子预测问题。在新模型中，应用模糊均值算法来处理模糊时间序列的区间划分，划分时考虑了数据点的性质，产生不等大小的区间。最后在仿真实验中采用真实的观察数据，仿真实验结果表明本文提出的预测模型在预测准确性方面要优于现有的其他预测模型。

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness in data sampling.There have been a variety of models developed to either improve forecasting accuracy or reduce computation overhead. However,the issues of controlling uncertainty in forecasting,effectively partitioning intervals,and consistently achieving forecasting accrucy with different interval lengths have been rarely investigated.In this paper,a novel forecasting model is proposed,because of the disadvantages of other existing forecasting models.A novel forecasting model enhances forecasting functionality and allows the  processing of twofactor forecasting problems.In addition,this model applies fuzzy Cmeans(FCM) clustering to deal with interval partitioning,which takes the nature of data points into account and produces unequalsized intervals.The superior accuracy of the proposed model is demonstrated by experiments by comparing it to other existing models using realworld empirical data.