Abstract
Along with the arrival of Industry 4.0 era, massive numbers of detecting instruments in various fields are continuously producing a plenty number of time series stream data. In order to efficiently and effectively analyze and mine the high-dimensional streaming time series, the segmentation which provides more accurate representation to the raw time series data, should be done as the first step. In this paper, we propose a novel online segmentation approach based on the turning points to partition the time series into some continuous subsequences and maintain a high similarity between the processed subsequences and the raw data. It achieves the best overall performance on the segmentation results compared with other baseline methods. Extensive experiments on all kinds of typical time series datasets have been conducted to demonstrate the advantages of our method.
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Acknowledgment
The authors would like to acknowledge the support provided by the Novel Software Technology Project(KFKT2015B02) and the Science & Technology Development Project of Shandong Province (2015GGX101009).
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hu, Y., Ji, C., Jing, M., Ding, Y., Kuai, S., Li, X. (2017). A Continuous Segmentation Algorithm for Streaming Time Series. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_13
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DOI: https://doi.org/10.1007/978-3-319-59288-6_13
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