The uncertain vector autoregressive model is able to model the interrelationships between different variables, which is more advantageous compared to the traditional autoregressive model, when modeling real-life objects and where the observed values are imprecise. In this paper, the parameters of the uncertain vector autoregressive model are estimated by using least absolute deviation estimation (LAD) to obtain a fitted uncertain vector autoregressive model, and residual analysis is performed to obtain estimates of expected values and variances of the residuals. In addition, future values are modeled by using forecasting methods, i.e., point estimation and interval estimation. The order of the uncertain vector autoregressive model is also determined by the indicator summation of test errors (STE) in the cross-validation, and we also analyze that the least absolute deviation estimation outperforms the least squares estimation method in the presence of outliers.

不确定向量自回归模型能够对不同变量之间的相互关系进行建模，在对现实生活中的对象进行建模且观测值不精确的情况下，与传统的自回归模型相比更具优势。本文利用最小绝对偏差估计（LAD）对不确定向量自回归模型的参数进行估计，得到拟合的不确定向量自回归模型，并进行残差分析以获得残差的期望值和方差的估计。此外，通过使用预测方法（即点估计和区间估计）对未来值进行建模。不确定向量自回归模型的阶数也由测试误差的指标总和决定（STE）在交叉验证中，我们还分析了在存在异常值的情况下，最小绝对偏差估计优于最小二乘估计方法。