pecuzal-embedding 1.2.1

PECUZAL automatic embedding of uni- and multivariate time series

PECUZAL Python

We introduce the PECUZAL automatic embedding of time series method for Python. It is solely based on the paper [kraemer2020] (Open Source), where the functionality is explained in detail. Here we give an introduction to its easy usage in three examples. Enjoy Embedding!

Getting started

Install from PyPI by simply typing

pip install pecuzal-embedding

in your console.

NOTE

This implementation is not profiled well. We recommend to use the implementation in the Julia language or in Matlab, in order to get fast results, especially in the multivariate case. Moreover, it is well documented and embedded in the DynamicalSystems.jl ecosystem. For instance, the compuations made in the Univariate example and the Multivariate example in this documentation took approximately 1500s (approx. 25 mins) and 7500s (approx. 2 hours!), respectively. In the Julia implementation the exact same computation took 3s and 20s, respectively! (running on a 2.8GHz Quad-Core i7, 16GB 1600 MHz DDR3)

Documentation

There is a documentation available including some basic usage examples.

Citing and reference

If you enjoy this tool and find it valuable for your research please cite

[kraemer2020]

Kraemer et al., “A unified and automated approach to attractor reconstruction”, arXiv:2011.07040 [physics.data-an], 2020.

or as BiBTeX-entry:

@misc{kraemer2020,
title={A unified and automated approach to attractor reconstruction},
author={K. H. Kraemer and G. Datseris and J. Kurths and I. Z. Kiss and J. L. Ocampo-Espindola and N. Marwan},
year={2020},
eprint={2011.07040},
archivePrefix={arXiv},
primaryClass={physics.data-an}
url={https://arxiv.org/abs/2011.07040}
}

Licence

This is program is free software and runs under MIT Licence.