Cardiovascular diseases are the first cause of death, globally. In such contexts an early diagnosis can be life-saving for most of the cases considered. The real problem is that, for a lot of arrhythmia types, there are not many data available, even in the most notorious dataset used in this matter. For these reason it is fundamentally important to create new beats that are similar to the available, but not too much in order to preserv the privacy of the patient. This is the task tackled in this work: use time-series forecasting techniques in order to create new heart-beats, simulating especially types of beats with some sort of cardiac anomaly.