  We give an introduction to a notion of weak dependence which is more general
than mixing and allows to treat for example processes driven by discrete
innovations as they appear with time series bootstrap. As a typical example, we
analyze autoregressive processes and their bootstrap analogues in detail and
show how weak dependence can be easily derived from a contraction property of
the process. Furthermore, we provide an overview of classes of processes
possessing the property of weak dependence and describe important probabilistic
results under such an assumption.
