in experiments, the dynamical behavior of systems is reflected in time seri
es. Due to the finiteness of the observational data set, it is not possible
to reconstruct the invariant measure up to an arbitrarily fine resolution
and an arbitrarily high embedding dimension. These restrictions limit our a
bility to distinguish between signals generated by different systems, such
as regular, chaotic, or stochastic ones, when analyzed from a time series p
oint of view. We propose to classify the signal behavior, without referring
to any specific model, as stochastic or deterministic on a certain scale o
f the resolution epsilon, according to the dependence of the (epsilon, tau)
entropy, h(epsilon, tau), and the finite size Lyapunov exponent lambda(eps
ilon) on epsilon.