基于二阶马尔可夫模型的模糊时间序列预测

针对当前模糊时间序列模型存在的缺乏有效论域划分方法和模糊关系前件多为一阶的现状，提出了基于二阶马尔可夫模型的模糊时间序列预测方法。应用模糊C均值聚类方法，获得序列中元素的隶属度；引入二阶马尔可夫模型中的转移概率矩阵表示模糊关系，更新了传统的模糊关系表示和运算；预测待求元素在各个模糊聚类的隶属度，并利用重心法去模糊化。将该模型运用到移动3G网络的性能预测中，与传统模糊时间序列预测方法相比，其准确性有了较大提高。

Second-order Markov model based fuzzy time series prediction

Specific to the lack of effective domain division method and much first-order fuzzy relationship, this paper proposes a second-order Markov model based fuzzy time series prediction method. It uses fuzzy C-means clustering to obtain the membership of elements in the time series. It introduces the transition matrix in second-order Markov model to represent fuzzy relations. It updates traditional representation and calculation of fuzzy relations. It forecasts the element’s membership in fuzzy clusters and defuzzifies the membership using the center-of-gravity method. It applies the model to the performance prediction of China Mobile 3G, and the accuracy is improved when compared to the traditional fuzzy time series prediction method.