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An Approach to Compress and Represents Time Series Data and Its Application in Electric Power Utilities

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Data Mining (AusDM 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 996))

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Abstract

This paper proposes a novel method that can reduce the volume of time series data adaptively and provide an alternate means in handling time series symbolically which can be used for time series’ classification or anomaly detection. The proposed method is tested using the time series data obtained from utility companies’ substations by comparing the compressed outputs to the original forms. The result is a new discretized set that is lower in volume and can represent the time series succinctly with a minimal loss that can be managed.

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Author information

Authors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

    Chee Keong Wee & Richi Nayak

Authors
  1. Chee Keong Wee
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  2. Richi Nayak
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Corresponding author

Correspondence to Chee Keong Wee .

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  2. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. Data Science and Engineering, Australian Taxation Office, Canberra, Australia

    Graco Warwick

  5. Department of Information Technology, University of Wollongong, Wollongong, NSW, Australia

    David Stirling

  6. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Chang-Tsun Li

  7. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Zahidul Islam

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© 2019 Springer Nature Singapore Pte Ltd.

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Wee, C.K., Nayak, R. (2019). An Approach to Compress and Represents Time Series Data and Its Application in Electric Power Utilities. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_9

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  • DOI: https://doi.org/10.1007/978-981-13-6661-1_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6660-4

  • Online ISBN: 978-981-13-6661-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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