NoLiTiA: An Open-Source Toolbox for Nonlinear Time Series Analysis

TL;DR

NoLiTiA is a comprehensive, open-source MATLAB toolbox that simplifies nonlinear time series analysis with a user-friendly interface, broad method implementation, and advanced analysis features for diverse scientific fields.

Contribution

It introduces a versatile MATLAB toolbox with extensive nonlinear analysis methods, including recent developments, accessible to users with minimal coding experience.

Findings

Includes a wide range of classic and new nonlinear analysis methods.

Provides spatially and time-resolved recurrence analysis.

Accessible through GUI and batch processing for non-programmers.

Abstract

In many scientific fields like e.g. neuroscience, climatology or physics, complex relationships can be described most parsimoniously by nonlinear mechanics. Despite their relevance, many scientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of nonlinear methods. Available packages mostly specialize in only one aspect of nonlinear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for nonlinear time series analysis. In comparison to other currently available nonlinear packages, NoLiTiA offers 1) an implementation of a broad range of classic and recently developed methods, 2) an implementation of newly proposed spatially and time-resolved recurrence analysis and 3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make nonlinear methods accessible to the broad scientific community engaged in time series processing.