skedm: Empirical Dynamic Modeling

Authors: Nicholas Cortale and Dylan McNamara

This pythonpackage implements nonlinear timeseries analysis techniques, also referred to as empirical dynamic modeling, based on many of the work flows and routines within TISEAN (Hegger and Schreiber 1999) and (Ye et al. 2017). The package provides a modern api, is written in pure python, and provides additional analysis routines not provided by TISEAN. skedm is capable of reconstructing statespaces from one, two, and even three-dimensional series. Additionally, it provides various methods for analyzing the evolution of nearby neighbors in the reconstructed state spaces. skedm also includes numerous one, two, and three-dimensional synthetic datasets for researchers to explore. The code makes use of scikit-learn’s (Pedregosaetal. 2011) efficient near neighbor implementation, and allows users familiar with the scikit-learn’s API (Buitincketal. 2013) to easily useskedm.