Information Theory is a branch of mathematics, more specifically probability theory, that studies information quantification. Recently, several researches have been successful with the use of Information Theoretic Learning (ITL) as a new technique of unsupervised learning. In these works, information measures are used as criterion of optimality in learning. In this article, we will analyze a still unexplored aspect of these information measures, their dynamic behavior. Autoregressive models (linear and non-linear) will be used to represent the dynamics in information measures. As a source of dynamic information, videos with different characteristics like fading, monotonous sequences, etc., will be used.