# Detecting dynamical changes in time series by using the Jensen Shannon Divergence

## TL;DR

This paper introduces methods based on Jensen-Shannon Divergence to detect dynamical changes in time series, effectively distinguishing between chaotic and random signals in both simulated and real data.

## Contribution

It proposes novel discretization schemes and detection techniques using Information Theory to identify transitions from chaotic to random regimes in time series.

## Key findings

- High accuracy in detecting dynamical changes
- Effective differentiation between chaotic and random signals
- Successful application to real-world data

## Abstract

Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide-band power spectrum, a delta-like autocorrelation function, and share other features as well. In general signals are presented as continuous records and require to be discretized for being analyzed. In this work we present different schemes for discretizing and for detection of dynamical changes in time series. One of the main motivations is to detect transition from chaotic regime to random regime. The tools used are originated in Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.