Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

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

  • @INPROCEEDINGS{10.1007/978-3-030-12981-1_33,
        author={Peng Zhan and Yupeng Hu and Wei Luo and Yang Xu and Qi Zhang and Xueqing Li},
        title={Feature-based Online Segmentation Algorithm for Streaming Time Series (Short Paper)},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={2},
        keywords={Data mining Streaming time series Online segmentation Algorithm},
        doi={10.1007/978-3-030-12981-1_33}
    }
    
  • Peng Zhan
    Yupeng Hu
    Wei Luo
    Yang Xu
    Qi Zhang
    Xueqing Li
    Year: 2019
    Feature-based Online Segmentation Algorithm for Streaming Time Series (Short Paper)
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-12981-1_33
Peng Zhan1, Yupeng Hu1,*, Wei Luo1, Yang Xu1, Qi Zhang1, Xueqing Li1,*
  • 1: Shandong University
*Contact email: huyupeng@sdu.edu.cn, xqli@sdu.edu.cn

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.