  We propose a new approach for properly analyzing stochastic time series by
mapping the dynamics of time series fluctuations onto a suitable nonequilibrium
surface-growth problem. In this framework, the fluctuation sampling time
interval plays the role of time variable, whereas the physical time is treated
as the analog of spatial variable. In this way we found that the fluctuations
of many real-world time series satisfy the analog of the Family-Viscek dynamic
scaling ansatz. This finding permits to use the powerful tools of kinetic
roughening theory to classify, model, and forecast the fluctuations of
real-world time series.
