How to handle machinery shut-down in Time Series for Anomaly Detection I have this data coming from sensors installed in a industrial machinery and my ultimate goal is implementing an anomaly detection method on it.
Now, the data is quite noisy and with lots of missing values and on top of that I have this regular pattern where every sunday the machine goes off and sensors either record a lower values than usual or they simply doesn't record any value at all.

My problem here is how should I treat this particular situation?
Remove completely sundays rows? Leave the data as is?
A little guidance on the matter would be much appreciated.

The sensors cannot record data while the machine is not running, so no data is actually "missing". We use this term for data that could have been observed but is not available for one reason or another (it wasn't collected in the first place, was lost, requires special permissions to access, etc.) Furthermore, it seems you know in advance when the machine is turned off (Sundays, public holidays) and no-one is going to need anomaly detection during the downtimes.
Instead, you have time series data that would require careful pre-preprocessing as it consists of multiple segments on (uninterrupted) machine operation. It might be necessary for example to pay special attention to the time right after the machine is restarted, if the sensor data looks "unusual" during the warm-up even though there are no anomalies.
