  A unified approach is proposed to describe the statistics of the short time
dynamics of multiscale complex systems. The probability density function of the
relevant time series (signal) is represented as a statistical superposition of
a large time-scale distribution weighted by the distribution of certain
internal variables that characterize the slowly changing background. The
dynamics of the background is formulated as a hierarchical stochastic model
whose form is derived from simple physical constraints, which in turn restrict
the dynamics to only two possible classes. The probability distributions of
both the signal and the background have simple representations in terms of
Meijer G-functions. The two universality classes for the background dynamics
manifest themselves in the signal distribution as two types of tails: power law
and stretched exponential, respectively. A detailed analysis of empirical data
from classical turbulence and financial markets shows excellent agreement with
the theory.
