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Diffusion entropy analysis

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Diffusion Entropy Analysis is a time-series analysis method for detecting temporal scaling in a data set, such as particle motion, a seismograph, or an electroencephalograph signal. Diffusion Entropy Analysis converts a timeseries into a diffusion trajectory and uses the entropy of this trajectory to measure the temporal scaling in the data. This is accomplished by moving a window along the trajectory, then using the relationship between the natural logarithm of the length of the window and the Shannon entropy to extract the scaling of the time-series process.

For further details about the method and how it works, please see Culbreth, G., Baxley, J. and Lambert, D., 2023. Detecting temporal scaling with modified diffusion entropy analysis. arXiv preprint arXiv:2311.11453.

Installation and use

The pymdea package is available on pypi and can be installed with uv:

uv add pymdea

or with pip:

pip install pymdea

A user guide is available in the documentation.

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numpy scipy polars matplotlib seaborn rich pytest ruff material for mkdocs mkdocstrings

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Modified diffusion entropy analysis. Time-series analysis technique developed by the Center for Nonlinear Science at the University of North Texas

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