Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These off-normal patterns are often referred to as anomalies, outliers, discordant observations, or exceptions in different application domains. The importance of anomaly detection is due to the fact that anomalies in data frequently involve significant and critical information in many application domains. In the particular case of nuclear fusion, there are a wide variety of anomalies that could be related to particular plasma behaviors, such as disruptions or L-H transitions. In the case of unknown anomalies, they probably represent the major proportion with respect to the total anomalies that can be found in fusion. Whether the anomaly is known or not, all the anomalies in a nuclear fusion device should be detected by using the same approach, i.e. the physical state of the plasma during a shot should be reflected in some of the thousands acquired signals. In this article, we study the application of Deep Learning and a particular recurrent neural network called LSTM to detect anomalies by forecasting in a discharge.