Publication Type:

Journal Article

Source:

The European Physical Journal Special Topics, Volume 226, p.2191-2204 (2017)

URL:

https://link.springer.com/article/10.1140/epjst/e2016-60397-x

Abstract:

Shannon Entropy has been extensively used for characterizing complexity of time series arising from chaotic dynamical systems and stochastic processes such as Markov chains. However, for short and noisy time series, Shannon entropy performs poorly. Complexity measures which are based on lossless compression algorithms are a good substitute in such scenarios. We evaluate the performance of two such Compression-Complexity Measures namely Lempel-Ziv complexity (LZ) and Effort-To-Compress (ETC) on short time series from chaotic dynamical systems in the presence of noise. Both LZ and ETC outperform Shannon entropy (H) in accurately characterizing the dynamical complexity of such systems. For very short binary sequences (which arise in neuroscience applications), ETC has higher number of distinct complexity values than LZ and H, thus enabling a finer resolution. For two-state ergodic Markov chains, we empirically show that ETC converges to a steady state value faster than LZ. Compression-Complexity Measures are promising for applications which involve short and noisy time series.

Cite this Research Publication

N. Nithin and Dr. Karthi Balasubramanian, “Dynamical Complexity Of Short and Noisy Time Series”, The European Physical Journal Special Topics, vol. 226, pp. 2191-2204, 2017.

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS