I am working with time series data of sensor measurements. I have nine sensors that are in the same ballpark location recording the same data every 10 minutes. The sensors are setup such that the readings should vary slightly given the setup of the experiment.
Some sensors experience power or other issues that causes them to record bad data. I would like to use the other data recordings and the trends associated, to be able to best detect outliers / anomalies from a given sensor. Ultimately I would like to label that data NaN. An example of the data I am looking at is provided here:
As is evident, one of the sensors is having an issue and is fixed. Is there a preferred method for detecting outliers like this? Is it acceptable to just do an IQR using all the data? I am working in Python with Pandas for my data analysis.