Probabilistic graphical models are a common choice for modeling domains that operate under uncertainty. Within this framework, Bayesian networks have become very popular due to their highly intuitive representation that makes their interpretation easy for someone unfamiliar with computational models. Temporal Nodes Bayesian Networks extend this concept by including a dynamic component to the model. Specifically, they model the relationships existing between events and when they occur. Recently, an algorithm for inducing a temporal nodes Bayesian network from a database was defined. This algorithm, denominated LIPS (Learning Interval, Parameters and Structure), implemented a novel strategy for learning the dynamic aspects of the model, while using traditional Bayesian network learning techniques for the structure and the parameters. Unfortunately, the results obtained by the LIPS algorithm depend heavily on the amount of data available for learning, and just as with many other traditional machine learning algorithms, learning from scarce data sets leads to unreliable models that produce poor results. Transfer learning is a paradigm that compensates for scarce data sets by borrowing information from other models that have already been learned. These auxiliary models differ from the target learning task, but hold some degree of similarity with it, such that the learning of certain aspects of the target model can be aided by this information transfer. Previously, work has been done for transfer learning in the area of probabilistic graphical models; however, the incorporation of transfer learning to dynamic models is relatively unexplored. In this thesis, a methodology for inducing a Temporal Nodes Bayesian Network using transfer learning is proposed. The algorithm defines a separate strategy for learning each component, where transfer learning is applied to induce the dynamic aspects of the model, as well as the structure and the parameters. Experimentation was carried out to evaluate the algorithm, and the results were compared to those obtained without transfer and by applying naive transfer. Results show that the proposed algorithm obtains models that are significantly better than those obtained by learning only from scarce data sets (without transfer). In addition, they show that the algorithm is able to retrieve reliable models even when few records are available for the task of interest.