NoLiTSA

NoLiTSA (NonLinear Time Series Analysis) is a Python module implementing several standard algorithms used in nonlinear time series analysis.

Features

Estimation of embedding delay using autocorrelation, delayed mutual information, and reconstruction expansion.
Embedding dimension estimation using false nearest neighbors and averaged false neighbors.
Computation of correlation sum and correlation dimension from both scalar and vector time series.
Estimation of the maximal Lyapunov exponent from both scalar and vector time series.
Generation of FT, AAFT, and IAAFT surrogates from a scalar time series.
Simple noise reduction scheme for filtering deterministic time series.
Miscellaneous functions for end point correction, stationarity check, fast near neighbor search, etc.

NoLiTSA can be installed using pip by running

git clone https://github.com/manu-mannattil/nolitsa.git
pip install ./nolitsa

or, with newer versions of pip:

pip install git+https://github.com/manu-mannattil/nolitsa.git

NoLiTSA requires NumPy and SciPy, and (theoretically) works with both Python 2 and 3.

NoLiTSA’s unit tests can be executed by running nosetests, or by using

python setup.py test

Versions of NoLiTSA were used in the following publications:

M. Mannattil, H. Gupta, and S. Chakraborty, “Revisiting Evidence of Chaos in X-ray Light Curves: The Case of GRS 1915+105,” Astrophys. J. 833, 208 (2016).
M. Mannattil, A. Pandey, M. K. Verma, and S. Chakraborty, “On the applicability of low-dimensional models for convective flow reversals at extreme Prandtl numbers,” Eur. Phys. J. B 90, 259 (2017)

Sagar Chakraborty is thanked for several critical discussions.

NoLiTSA is licensed under the 3-clause BSD license. See the file LICENSE for more details.