Metadata
- Author: Vincent Granville
- Full Title:: Defining and Measuring Chaos in Data Sets: Why and How, in Simple Words
- Category:: 🗞️Articles
- URL:: https://www.datasciencecentral.com/defining-and-measuring-chaos-in-data-sets-why-and-how-in-simple-w/
- Finished date:: 2024-02-01
Highlights
The Hurst exponent H is used to measure the level of smoothness in time series, and in particular, the level of long-term memory. H takes on values between 0 and 1, with H = 1/2 corresponding to the Brownian motion, and H = 0 corresponding to pure white noise (View Highlight)
In dynamical systems, the Lyapunov exponent is used to quantify how a system is sensitive to initial conditions. Intuitively, the more sensitive to initial conditions, the more chaotic the system is (View Highlight)
the approximate entropy is a metric used to quantify regularity and predictability in time series fluctuations (View Highlight)