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Analysing a Periodical and Multi-dimensional Time Series

  • Octavian Lucian HasnaEmail author
  • Rodica PotoleaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)

Abstract

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.

Keywords

Time series analysis Fault diagnosis Data mining 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Technical University of Cluj-NapocaCluj-NapocaRomania

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