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• W3208348525 startingPage "1407" @default.
• W3208348525 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." @default.
• W3208348525 created "2021-11-08" @default.
• W3208348525 creator A5000931605 @default.
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• W3208348525 date "2021-10-29" @default.
• W3208348525 modified "2023-10-16" @default.
• W3208348525 title "Detecting a multigranularity event in an unequal interval time series based on self-adaptive segmenting" @default.
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• W3208348525 doi "https://doi.org/10.3233/ida-205480" @default.
• W3208348525 hasPublicationYear "2021" @default.
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