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Today's hybrid technical systems, such as production facilities, evolve rapidly. Therefore the efforts and resources needed to obtain their trustworthy behavior models are significantly increased. This forms the modeling bottleneck, as the model creation mostly relies on the expensive and durable manual modeling. Behavior models are of the greatest importance in monitoring, anomaly detection, and diagnosis applications. To deal with this issue, the novel HyBUTLA algorithm was recently proposed for automated learning of behavior models from process data. Despite being the first hybrid automaton learning algorithm, it does not model the autonomous jumps (abrupt changes in process variables), and it suffers from long runtime due to the use of advanced machine learning methods. In this paper the HyBUTLA algorithm is improved to account for the autonomous jumps by introducing the splitting step based on discrete wavelet transform. This significantly improves the algorithm runtime, since high function approximation accuracy can be reached by using rather simple methods such as multiple linear regression. The benefits that splitting brings are formally proved and demonstrated in a real-world production system.