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Modeling and detecting bursts in data streams is an important area of research with a wide range of applications. In this paper, we present a novel method to analyze and identify correlated burst patterns by considering multiple data streams that coevolve over time. The main technical contribution of our research is the use of a dynamic probabilistic network to model the dependency structures observed within these data streams. Such dependencies provide meaningful information concerning the overall system dynamics and should be explicitly integrated into the burst detection process. Using both synthetic scenarios and two real-world datasets, we compare our method with an existing burst-detection algorithm. Initial experimental results indicate that our approach allows for more balanced and accurate burst quantification.