This paper is concerned with the discovering of temporal knowledge from a sequence of timed observations provided by a system monitoring of dynamic process. The discovering process is based on the Stochastic Approach framework where a series of timed observations is represented with a Markov chain. From this representation, a set of timed sequential binary relations between discrete event classes is discovered with an abductive reasoning and represented as abstract chronicle models. To reduce the search space as close as possible to the potential relations between the process variables, we propose to characterize a set of series of timed observations with a unique measure of the homogeneity of the crisscross of class occurrences and to use this measure to prune abstract chronicle models.