### Abstract

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, providing a compact representation that aims to keep 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. The chapter includes examples of application and source codes to get familiar with these techniques.

### Bibtex reference

@incollection{Calinon19MM,
author="Calinon, S.",
title="Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series",
booktitle="Mixture Models and Applications",
publisher="Springer, Cham",
editor="Bouguila, N. and Fan, W.",
year="2019",
pages="39--57",
doi="10.1007/978-3-030-23876-6_3"
}