Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data.

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough-compared with the rest of the data set that is being analyzed-and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account.