Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data, have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback. This paper presents a method to learn genetic network connectivity which exploits the time series nature of experimental data to achieve better causal predictions. This method breaks up the data into bins, and determines an initial set of potential influence vectors for each gene based upon the probability of the gene’s expression increasing in the next time step. These vectors are then combined to form new vectors with better scores and are competed against each other to determine the final influence vector for each gene. The result is a directed graph representation of the genetic network’s repression and activation connections. Results are reported for several synthetic networks with tight feedback showing significant improvements over another dynamic Bayesian approach. Promising results are reported for genes involved in the yeast cell cycle.