@gerardo, IMHO your use-case falls more in the probabilistic forecasting. It’s a savant word to say that instead of predicting one value per time step, you predict a range of values inside a certain percentile : Image please:
As you can see our predictions fall in different percentile intervals. You can then decide that if a prediction falls outside the 90% percentile (for example), it will be considered as an anomaly and you trigger an alert for example. Bear in mind, this a simplified explanation, and one way to do anomaly detection 
. Another way would be using time series classification. However, in your case you are also forecasting your time series
You may check @takotab time series forecasting module fastseq (see here above) that he implemented in fastai v2 (it’s for univariate time series (one variable)) or Amazon Labs’ GluonTS Tutorial. I talked about it here
You can also search on Google for time series forecasting anomaly. Be prepared, there is a lot of information.