In the era of data-driven applications, time series data is prolific, particularly in domains involving sensor-monitored systems. Such systems often produce a stream of multivariate time series data that can be leveraged to predict malfunctions and failures or, more broadly, anomalous events. Prediction of anomalies in time series data is a challenge that has been extensively pursued by researches, as failure forecast could save crucial resources in most applications. In this thesis, classical approaches to this problem will be discussed and a new approach that leverages graph convolutional neural networks will be presented. Such approach enables a much more in-depth analysis of multivariate time series since it allows to detect not only temporal correlations but also correlations between features.