Compound method of time series classification
Łukasz Korus
Wroclaw University of Technology, Poland
Michał Piórek
Wroclaw University of Technology, Poland
Published 2019-07-26


deterministic chaos
time series analysis
Takens theorem

How to Cite

Korus Łukasz and Piórek M. (2019) “Compound method of time series classification”, Nonlinear Analysis: Modelling and Control, 20(4), pp. 545-560. doi: 10.15388/NA.2015.4.6.


Many real phenomenona preserves the properties of chaotic dynamics. However, unambiguous determination of belonging to a group of chaotic systems is difficult and complex problem. The main purpose of this paper is to present compound method of time series classification which is basically directed to the detection of chaotic behaviors. The method has been designed for differentiation of three types of time series: chaotic, periodic and random. Our approach assumes, that more reliable information about the dynamics of the system will provide the compilation of several methods, than any individual. This paper focuses on choosing a good set of methods and analysis of their results. In our investigation, we used the following methods and indicators: time delay embedding, mutual information, saturation of system invariants, the largest Lyapunov exponent and Hurst exponent. We checked the validity of the methods applying them to three kinds of basic systems which generate chaotic, periodic and random time series. As a summary of this paper, all selected methods and indicators computed for generated times series have been summarized in the table, which gives the authors a possibility to conclude about type of observed behavior.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Please read the Copyright Notice in Journal Policy