LMC and SDL Complexity Measures: A Tool to Explore Time Series

Complexity 2019 (1):2095063 (2019)
  Copy   BIBTEX

Abstract

This work is a generalization of the López-Ruiz, Mancini, and Calbet (LMC) and Shiner, Davison, and Landsberg (SDL) complexity measures, considering that the state of a system or process is represented by a continuous temporal series of a dynamical variable. As the two complexity measures are based on the calculation of informational entropy, an equivalent information source is defined by using partitions of the dynamical variable range. During the time intervals, the information associated with the measured dynamical variable is the seed to calculate instantaneous LMC and SDL measures. To show how the methodology works generating indicators, two examples, one concerning meteorological data and the other concerning economic data, are presented and discussed.

Other Versions

No versions found

Links

PhilArchive

    This entry is not archived by us. If you are the author and have permission from the publisher, we recommend that you archive it. Many publishers automatically grant permission to authors to archive pre-prints. By uploading a copy of your work, you will enable us to better index it, making it easier to find.

    Upload a copy of this work     Papers currently archived: 106,169

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Analytics

Added to PP
2019-01-04

Downloads
24 (#1,005,806)

6 months
6 (#724,158)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

No references found.

Add more references