Context-aware edge similarity segmentation algorithm of time series

• Published in: Cluster computing Vol. 19; no. 3; pp. 1421 - 1436
• Main Authors: Wang, Lei, Xu, Lingyu, Yu, Jie, Xue, Yunlan, Zhang, Gaowei
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Big Data; Clustering; Computer Communication Networks; Computer Science; Context; Economic models; Failure rates; Methods; Operating Systems; Pattern matching; Process controls; Processor Architectures; Public opinion; Segmentation; Similarity; Temperature; Time series; Edge density; Clustering; Edge similarity; Time series
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-016-0604-7

This paper proposes a method of context-aware edge similarity segmentation algorithm of time series. The algorithm overcomes the machinery of traditional pattern discovery and traditional pattern matching and breaks the limitation of the fixed mode of boundary movement. The algorithm also has stronger adaptive capacity in the humanities sequence activities based on the network. Considering the edge connected by the adjacent points of time series as a fragment, and combining the similarity between the edges calculated by context relation to extract edge density, we can obtain data groups with similar characteristics with the clustering algorithm. This method can not only find the implicit relationships between edges, but also can increase time series segmentation robustness. Experimental results show that our algorithm can reduce the failure rate of segments caused by only considering the pattern matching, and has better anti-interference performance in noisy environments.