To improve the efficiency of segmentation methods for multivariate time series, a hybrid dynamic learning mechanism for such series' segmentation is proposed. First, an incremental clustering algorithm is used to automatically cluster variables of multivariate time series. Second, common factors are extracted from every cluster by a dynamic factor model as an ensemble description of the system. Third, this common factor series is segmented by dynamic programming. The proposed method can potentially segment multivariate time series and not only performs segmentation better on multivariate time series with a large number of variables but also improves the running accuracy and efficiency of the algorithm, especially when analyzing complex datasets.

中文翻译：

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多元时间序列分割的混合动态学习机制

为了提高多元时间序列分割方法的效率，提出了一种用于该序列分割的混合动态学习机制。首先，使用增量聚类算法自动聚类多元时间序列的变量。其次，通过动态因子模型从每个群集中提取公共因子，作为系统的整体描述。第三，通过动态规划将这个公共因子系列进行细分。所提出的方法可以潜在地对多元时间序列进行分割，不仅可以对具有大量变量的多元时间序列进行更好的分割，而且可以提高算法的运行精度和效率，尤其是在分析复杂数据集时。