We propose a new sequential monitoring scheme for changes in the parameters of a multivariate time series. In contrast to procedures proposed in the literature which compare an estimator from the training sample with an estimator calculated from the remaining data, we suggest to divide the sample at each time point after the training sample. Estimators from the sample before and after all separation points are then continuously compared calculating a maximum of norms of their differences. For open-end scenarios our approach yields an asymptotic level $\alpha$ procedure, which is consistent under the alternative of a change in the parameter. By means of a simulation study it is demonstrated that the new method outperforms the commonly used procedures with respect to power and the feasibility of our approach is illustrated by analyzing two data examples.

中文翻译：

---

一种开放式顺序变化点监测的新方法

我们为多元时间序列的参数变化提出了一种新的顺序监测方案。与文献中提出的将来自训练样本的估计量与根据剩余数据计算出的估计量进行比较的程序相比，我们建议在训练样本之后的每个时间点划分样本。然后连续比较来自所有分离点之前和之后的样本的估计值，计算它们差异的最大范数。对于开放式场景，我们的方法产生了一个渐近级 $\alpha$ 过程，它在参数变化的替代方案下是一致的。