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Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases

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  • Kugiumtzis, Dimitris
  • Tsimpiris, Alkiviadis

Abstract

In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series (MATS) MATLAB toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of MATS are presented and the implemented measures are briefly described. The usefulness of MATS is illustrated on some empirical examples along with screenshots.

Suggested Citation

  • Kugiumtzis, Dimitris & Tsimpiris, Alkiviadis, 2010. "Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i05).
    • Handle: RePEc:jss:jstsof:v:033:i05
    DOI: http://hdl.handle.net/10.18637/jss.v033.i05
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    1. Marc Hallin & Madan Lal Puri, 1992. "Rank tests for time-series analysis: a survey," ULB Institutional Repository 2013/2229, ULB -- Universite Libre de Bruxelles.
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    4. Chen, Wei-Shing, 2011. "Use of recurrence plot and recurrence quantification analysis in Taiwan unemployment rate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1332-1342.
    5. Iliopoulos, A.C. & Nikolaidis, N.S. & Aifantis, E.C., 2015. "Portevin–Le Chatelier effect and Tsallis nonextensive statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 509-518.
    6. Kostić, Srđan & Vasović, Nebojša & Perc, Matjaž & Toljić, Marinko & Nikolić, Dobrica, 2013. "Stochastic nature of earthquake ground motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4134-4145.

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