  Deterministic chaos is phenomenon from nonlinear dynamics and it belongs to
greatest advances of twentieth-century science. Chaotic behavior appears apart
of mathematical equations also in wide range in observable nature, so as in
there originating time series. Chaos in time series resembles stochastic
behavior, but apart of randomness it is totally deterministic and therefore
chaotic data can provide us useful information. Therefore it is essential to
have methods, which are able to detect chaos in time series, moreover to
distinguish chaotic data from stochastic one. Here we present and discuss the
performance of standard and machine learning methods for chaos detection and
its implementation on two well known simple chaotic discrete dynamical systems
- Logistic map and Tent map, which fit to the most of the definitions of chaos.
