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Temporal data mining aims at finding patterns in historicaldata. Our work proposes an approach to extract temporalpatterns from data to predict the occurrence of targetevents, such as computer attacks on host networks, or fraudulenttransactions in financial institutions. Our problemformulation exhibits two major challenges: 1) we assumeevents being characterized by categorical features and displayinguneven inter-arrival times; such an assumption fallsoutside the scope of classical time-series analysis, 2) weassume target events are highly infrequent; predictive techniquesmust deal with the class-imbalance problem. We pro-posean efficient algorithm that tackles the challenges aboveby transforming the event prediction problem into a searchfor all frequent eventsets preceding target events. The classimbalance problem is overcome by a search for patterns onthe minority class exclusively; the discrimination power ofpatterns is then validated against other classes. Patternsare then combined into a rule-based model for prediction.Our experimental analysis indicates the types of event sequenceswhere target events can be accurately predicted.