Uncertain vector autoregressive model (UVAR) is used to describe the variational relationship between multivariable time series. Based on the imprecise observations, the issue of predicting the future data accurately attracts more and more scholars attentions. This paper takes the ridge estimation into consideration and applies it into uncertain vector autoregressive model. In order to determine the shrinkage parameter, we use the cross-validation to solve this problem. On this basis, we conduct the residual analysis. A point estimation and a confidence interval are given to predict the future value. Finally, two numerical examples are applyied to clarify the feasibility and validity of the ridge estimation, compared with the least square estimation.

不确定向量自回归模型（UVAR）用于描述多变量时间序列之间的变分关系。基于不精确的观测，准确预测未来数据的问题引起了越来越多学者的关注。本文考虑岭估计并将其应用于不确定向量自回归模型。为了确定收缩参数，我们使用交叉验证来解决这个问题。在此基础上，我们进行残差分析。给出点估计和置信区间来预测未来值。最后，通过两个数值例子来阐明岭估计与最小二乘估计的可行性和有效性。