Practical implementation of nonlinear time series methods: The <scp>TISEAN</scp> package

TLDR

The paper references existing literature on the theoretical background of nonlinear time series analysis. The study describes implementing nonlinear time series analysis methods grounded in deterministic chaos. The authors implement a suite of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing, illustrating each with typical applications and discussing implementation choices. The resulting strategies are implemented in the publicly available TISEAN software package. © 1999 American Institute of Physics.

Abstract

We describe the implementation of methods of nonlinear time series analysis which are based on the paradigm of deterministic chaos. A variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing are discussed with particular emphasis on issues of implementation and choice of parameters. Computer programs that implement the resulting strategies are publicly available as the TISEAN software package. The use of each algorithm will be illustrated with a typical application. As to the theoretical background, we will essentially give pointers to the literature. (c) 1999 American Institute of Physics.