Dispersion Entropy: A Measure for Time-Series Analysis

Rostaghi, Mostafa; Azami, Hamed

doi:10.1109/LSP.2016.2542881

Record: found
Abstract: not found
Article: not found

Dispersion Entropy: A Measure for Time-Series Analysis

Author(s): Mostafa Rostaghi , Hamed Azami

Publication date (Print): May 2016

Journal: IEEE Signal Processing Letters

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Read this article at

ScienceOpenPublisher

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Related collections

Most cited references 18

Record: found
Abstract: found
Article: not found

Permutation Entropy: A Natural Complexity Measure for Time Series

Christoph Bandt, Bernd Pompe (2002)

We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.