The INFN CNAF data center provides a huge amount of heterogeneous data through the adoption
of dedicated monitoring systems. Having to provide a 24/7 availability, it has started to assess
artificial intelligence solutions to detect anomalies aimed to predict possible failures.
In this study, the main goal is to define an artificial intelligence framework able to classify and
predict anomalies in time series data obtained from different sensors and systems within the data
center (i.e., electrical plant, cooling system, and UPS system). Having to deal with unlabeled
data, the proposed framework performs as a first step a regression task to learn the behavior of
the sensors and, given the previous 5 timestamps, provides the values of the sensors in the next
timestamp. As a second step, it performs a classification task. Comparing the predicted and the
actual behaviors of the sensors, in fact, evaluates the status of the system and possible anomalies.
During the first step, a mean squared error of 0.025 has been obtained, while in the second one an
F1-score of 0.997 has been reached.