Frequent Patterns Discovery and Analysis Granularity Recognition for Shanghai Composite Index Sequence

被引：0
|
作者：
Gao Xuedong [1 ]
Chen Hailan [1 ]
机构：
[1] Univ Sci & Technol Beijing, Donlinks Sch Econ & Management, Beijing, Peoples R China
来源：
2016 INTERNATIONAL CONFERENCE ON LOGISTICS, INFORMATICS AND SERVICE SCIENCES (LISS' 2016) | 2016年
关键词：
frequent patterns; time granularity; agglomerative hierarchical clustering; Dynamic Time Warping;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The Shanghai Composite Index is a typical time series, and reflects the stock market variations. In this paper, a method is proposed for mining frequent patterns from the Shanghai Composite Index using the agglomerative hierarchical clustering algorithm based on DTW. For reflecting the nature of research question and reducing the computational complexity, the best time granularity is found by effective evaluation based on the clustering results.
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页数：7
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