  This chapter presents an overview of techniques used for the analysis,
edition, and synthesis of time series, with a particular emphasis on motion
data. The use of mixture models allows the decomposition of time signals as a
superposition of basis functions. It provides a compact representation that
aims at keeping the essential characteristics of the signals. Various types of
basis functions have been proposed, with developments originating from
different fields of research, including computer graphics, human motion
science, robotics, control, and neuroscience. Examples of applications with
radial, Bernstein and Fourier basis functions will be presented, with
associated source codes to get familiar with these techniques.
