The inference mechanisms of CEP engines are completely guided by rules, which are specified manually by domain experts. We argue that this user-based rule specification is a limiting factor, as it requires the experts to have technical knowledge about the CEP language they want to use, it restricts the usage of CEP to merely the detection of straightforward situations, and it restrains its propagation to more advanced fields that require earliness, prediction and proactivity. Therefore, we introduce autoCEP as a data mining-based approach that automatically learns CEP rules from historical traces. autoCEP requires no technical knowledge from domain experts, and it also shows that the generated rules fit for prediction and proactive applications. Satisfactory results from evaluations on real data demonstrate the effectiveness of our framework.