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

  • Chee Keong WeeEmail author
  • Richi Nayak
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)


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.


Time series compression Time series representation 


  1. 1.
    Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. (CSUR) 45(1), 12 (2012)CrossRefGoogle Scholar
  2. 2.
    Bagnall, A., et al.: A bit level representation for time series data mining with shape-based similarity. Data Min. Knowl. Discov. 13(1), 11–40 (2006)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zhu, Z., et al.: Time series mining based on multilayer piecewise aggregate approximation, pp. 174–179. IEEE (2016)Google Scholar
  4. 4.
    Wu, Y.-L., Agrawal, D., El Abbadi, A.: A comparison of DFT and DWT based similarity search in time-series databases. In: Proceedings of the Ninth International Conference on Information and Knowledge Management. ACM (2000)Google Scholar
  5. 5.
    Notaristefano, A., Chicco, G., Piglione, F.: Data size reduction with symbolic aggregate approximation for electrical load pattern grouping. IET Gener. Transm. Distrib. 7(2), 108–117 (2013)CrossRefGoogle Scholar
  6. 6.
    Toshniwal, D., Joshi, R.C.: Finding similarity in time series data by method of time weighted moments. In: Proceedings of the 16th Australasian Database Conference, vol. 39. Australian Computer Society, Inc. (2005)Google Scholar
  7. 7.
    Keogh, E.: A decade of progress in indexing and mining large time series databases. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB Endowment (2006)Google Scholar
  8. 8.
    Keogh, E., et al.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Sigmod Rec. 30(2), 151–162 (2001)CrossRefGoogle Scholar
  9. 9.
    Tang, Q., et al.: Typical power load profiles shape clustering analysis based on adaptive piecewise aggregate approximation. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing (2018)Google Scholar
  10. 10.
    Paparrizos, J., Gravano, L.: k-shape: efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM (2015)Google Scholar
  11. 11.
    Eichinger, F., et al.: A time-series compression technique and its application to the smart grid. VLDB J. 24(2), 193–218 (2015)CrossRefGoogle Scholar
  12. 12.
    Burtini, G., Fazackerley, S., Lawrence, R.: Time series compression for adaptive chart generation. In: 2013 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE (2013)Google Scholar
  13. 13.
    Ning, J., et al.: A wavelet-based data compression technique for smart grid. IEEE Trans. Smart Grid 2(1), 212–218 (2011)CrossRefGoogle Scholar
  14. 14.
    Guo, C., Li, H., Pan, D.: An improved piecewise aggregate approximation based on statistical features for time series mining. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS (LNAI), vol. 6291, pp. 234–244. Springer, Heidelberg (2010). Scholar
  15. 15.
    McLoughlin, F., Duffy, A., Conlon, M.: Evaluation of time series techniques to characterise domestic electricity demand. Energy 50, 120–130 (2013)CrossRefGoogle Scholar
  16. 16.
    Badea, I., Trausan-Matu, S.: Text analysis based on time series, pp. 37–41. IEEE (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Science and Engineering FacultyQueensland University of TechnologyBrisbaneAustralia

Personalised recommendations