Method for Treating Anomalies in Multivariate Time Series

In recent years, the classification of time series has gained great relevance in significant sectors and segments of society. Machine Learning Techniques make it possible to interpret the behavior of anomalous phenomena in multivariate datasets. This work proposes a study of three methods from the perspective of their ability to provide relevant information for the detection, validation and prediction of anomalous events in time series data. To achieve this goal, a case study was carried out exploring algorithms based on neural networks and inductive symbolic learning applied to a real problem of detecting anomalies associated with the oil well drilling process. The main results indicate that this method can be a promising way to treat anomalies.