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