The starting point to study many dynamical phenomena in nature is a set of measurements of some representative variable of interest at discrete time intervals, i.e. a \"black box\" time series. When the underlying dynamics is simple, traditional and standard tools, like Fourier tranform, can be used to make inferences and characterize the behavior. However, for complex dynamics more sophisticated approaches are necessary. In this seminar I will introduce two novel information theory quantifiers: permutation entropy and statistical complexity. Useful information about the system\'s dynamics can be derived from them. Moreover, they can be easily estimated and are robust under the presence of additive noise. Several applications, from the analysis of numerical and experimental time series, will be shown.