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Article type: Research Article
Authors: Li, Haibo* | Yu, Yongbo
Affiliations: College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China
Correspondence: [*] Corresponding author: Haibo Li, College of Computer Science and Technology, Huaqiao University, No. 668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China. Tel.: +86 592 6162556; Fax: +86 592 6162559; E-mail: lihaibo@hqu.edu.cn.
Abstract: Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.
Keywords: Event detection, multigranularity, time series, symbol sequence, self-adaptive segment
DOI: 10.3233/IDA-205480
Journal: Intelligent Data Analysis, vol. 25, no. 6, pp. 1407-1429, 2021
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