Skip to main content

Analysing a Periodical and Multi-dimensional Time Series

  • Conference paper
  • First Online:
Mining Intelligence and Knowledge Exploration (MIKE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11308))


Time series analysis has become an important field of data mining in the last decade. Dynamics of real-world processes are important in domains like seismology, medicine, astrophysics, meteorology, economics and industry. In this article we develop a methodology for analysing a periodical and multi-dimensional time series so as to extract new features that improve the performance of time series classification. Our aim is to have a methodology which is independent on the measurement errors and on the level of noise. For this we analyse three methods for extracting a period, both from the perspective of methodology and performance. We experimentally compare these strategies in order to identify the minimal quantity of labelled time series required for training so as to obtain a good classification accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE. Trans. Comput. 100(1), 90–93 (1974)

    Article  MathSciNet  Google Scholar 

  2. Bagnall, A., Bostrom, A., Large, J., Lines, J.: The great time series classification bake off: An experimental evaluation of recently proposed algorithms. extended version (2016)

    Google Scholar 

  3. Cardoso, A.M., Cruz, S., Carvalho, J., Saraiva, E.: Rotor cage fault diagnosis in three-phase induction motors, by park’s vector approach. In: IEEE Industry Applications Conference, vol. 1, pp. 642–646. IEEE (1995)

    Google Scholar 

  4. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases, vol. 23. ACM (1994)

    Google Scholar 

  5. Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)

    Article  Google Scholar 

  6. Fulcher, B.D.: Feature-based time-series analysis (2017)

    Google Scholar 

  7. Haar, A.: Zur theorie der orthogonalen funktionensysteme 69(3), 331–371 (1910)

    Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

    Article  Google Scholar 

  9. Hasna, O.L., Potolea, R.: Time series–a taxonomy based survey. In: Intelligent Computer Communication and Processing (ICCP), pp. 231–238. IEEE (2017)

    Google Scholar 

  10. Martin-Diaz, I., Morinigo-Sotelo, D., Duque-Perez, O., Osornio-Rios, R.A., Romero-Troncoso, R.J.: Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors. ISA Trans. 80, 427–438 (2018)

    Article  Google Scholar 

  11. Mitsa, T.: Temporal Data Mining. Chapman and Hall/CRC, Boca Raton (2010)

    Book  Google Scholar 

  12. Nandi, S., Toliyat, H., Li, X.: Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)

    Article  Google Scholar 

  13. Rajakarunakaran, S., Venkumar, P., Devaraj, D., Rao, K.S.P.: Artificial neural network approach for fault detection in rotary system. Appl. Soft Comput. 8(1), 740–748 (2008)

    Article  Google Scholar 

  14. Timmer, J., Gantert, C., Deuschl, G., Honerkamp, J.: Characteristics of hand tremor time series. Biol. Cybern. 70(1), 75–80 (1993)

    Article  Google Scholar 

  15. Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Disc. 26(2), 275–309 (2013)

    Article  MathSciNet  Google Scholar 

  16. Xu, B., Sun, L., Xu, L., Xu, G.: Improvement of the hilbert method via esprit for detecting rotor fault in induction motors at low slip. IEEE Trans. Energy Convers. 28(1), 225–233 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Octavian Lucian Hasna or Rodica Potolea .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hasna, O.L., Potolea, R. (2018). Analysing a Periodical and Multi-dimensional Time Series. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05917-0

  • Online ISBN: 978-3-030-05918-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics