  The notion of drift refers to the phenomenon that the distribution, which is
underlying the observed data, changes over time. Albeit many attempts were made
to deal with drift, formal notions of drift are application-dependent and
formulated in various degrees of abstraction and mathematical coherence. In
this contribution, we provide a probability theoretical framework, that allows
a formalization of drift in continuous time, which subsumes popular notions of
drift. In particular, it sheds some light on common practice such as
change-point detection or machine learning methodologies in the presence of
drift. It gives rise to a new characterization of drift in terms of stochastic
dependency between data and time. This particularly intuitive formalization
enables us to design a new, efficient drift detection method. Further, it
induces a technology, to decompose observed data into a drifting and a
non-drifting part.
