信度加权马尔科夫链预测模型及应用

Reliability Weighted Markov Chain Prediction Model and Its Application

利用D-S理论对原始数据建立信度函数,划分状态区间,并对不同时期历史数据进行加权,以规范化的各阶自相关系数为权重,创建加权马尔科夫链预测模型.通过特征值法对预测对象进行定量分析,计算具体预测值.根据实例进行实证对比分析,得到了较小的预测误差.

Using the D-S theory to establish the reliability function of the original data and divide the state interval,a weighted Markov chain prediction model was established by weighting the historical data of different periods and taking the normalized autocorrelation coefficients of each order as weights.The prediction object was quantitatively analyzed by eigenvalue method and the specific prediction value was calculated.The empirical comparative analysis was carried out according to the example,and the smaller prediction error was obtained.