التفاصيل البيبلوغرافية
| العنوان: |
Hybrid dynamic learning mechanism for multivariate time series segmentation. |
| المؤلفون: |
Wang, Ling, Li, Kang, Ma, Qian, Lu, YanRong |
| المصدر: |
Statistical Analysis & Data Mining; Apr2020, Vol. 13 Issue 2, p165-177, 13p |
| مصطلحات موضوعية: |
BLENDED learning, DYNAMIC programming, DYNAMIC models |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
