Entropy rates will be considered as a tool for quantitative characterization of dynamic processes evolving in time. Let be a time series, i.e., a series of measurements done on a system in consecutive instants of time . The time series can be considered as a realization of a stochastic process , characterized by the joint probability distribution function , Pr . The entropy rate of is defined as :
where is the entropy of the joint distribution :
Alternatively, the time series can be considered as a projection of a trajectory of a dynamical system, evolving in some measurable state space. As a definition of the entropy rate of a dynamical system, known as the Kolmogorov-Sinai entropy (KSE) [2, 3, 4] we can consider the equation (1), however, the variables should be understood as m-dimensional variables, according to a dimensionality of the dynamical system . If the dynamical system is evolving in a continuous measure space, then any entropy depends on a partition chosen to discretize the space and the KSE is defined as a supremum over all finite partitions [2, 3, 4].
The KSE is a topological invariant, suitable for classification of dynamical systems or their states, and is related to the sum of the system's positive Lyapunov exponents (LE) according to the theorem of Pesin .
A number of algorithms (see, e.g., [7, 8, 9, 10] and references therein) have been proposed for estimation of the KSE from time series. Reliability of these estimates, however, is limited  by available amount of data, finite precision measurements and noise always present in experimental data. No general approach to estimating the entropy rates of stochastic processes has been established, except of simple cases such as finite-state Markov chains . However, if is a zero-mean stationary Gaussian process with spectral density function , its entropy rate , apart from a constant term, can be expressed using as [12, 13, 14]:
Dynamics of a stationary Gaussian process is fully described by its spectrum. Therefore the connection (3) between the entropy rate of such a process and its spectral density is understandable. The estimation of the entropy rate of a Gaussian process is reduced to the estimation of its spectrum.
If a studied time series was generated by
a nonlinear, possibly chaotic, dynamical system,
its description in terms of a spectral density
is not sufficient. Indeed,
realizations of isospectral Gaussian
processes are used in the surrogate-data based tests
in order to discern nonlinear (possibly chaotic)
processes from colored noises [15, 16].
On the other hand, there are results indicating that
some characteristic properties of nonlinear
dynamical systems may be ``projected'' into
their ``linear properties'', i.e., into spectra,
into autocorrelation functions:
Sigeti  has demonstrated that
there may be a relation between the sum of
positive Lyapunov exponents
of a chaotic dynamical system
and the decay coefficient characterizing the
exponential decay at high frequencies of spectra
estimated from time series generated by the dynamical
system. Asymptotic decay of autocorrelation
functions of such time series is ruled
by the second eigenvalue of the Perron-Frobenius
operator of the dynamical system [18, 19].
Lipton & Dabke  have also investigated
asyptotic decay of spectra in relation to properties
of underlying dynamical systems.