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Feature-based Online Segmentation Algorithm for Streaming Time Series (Short Paper)

  • Peng Zhan
  • Yupeng HuEmail author
  • Wei Luo
  • Yang Xu
  • Qi Zhang
  • Xueqing LiEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

Over the last decade, huge number of time series stream data are continuously being produced in diverse fields, including finance, signal processing, industry, astronomy and so on. Since time series data has high-dimensional, real-valued, continuous and other related properties, it is of great importance to do dimensionality reduction as a preliminary step. In this paper, we propose a novel online segmentation algorithm based on the importance of TPs to represent the time series into some continuous subsequences and maintain the corresponding local temporal features of the raw time series data. To demonstrate the advantage of our proposed algorithm, we provide extensive experimental results on different kinds of time series datasets for validating our algorithm and comparing it with other baseline methods of online segmentation.

Keywords

Data mining Streaming time series Online segmentation Algorithm 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.School of SoftwareShandong UniversityJinanChina
  2. 2.School of Computer Science and TechnologyShandong UniversityQingdaoChina

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