Setup the anomaly detection module for high frequency, non cyclical data I have a dataset with 500 cycles of an industrial process, these samples have a duration of 8 seconds at a sampling rate of 4 milliseconds, that accounts for 2048 samples for each cycle.
The sampling events are unevenly distributed in time, as shown in the image below where he green blocks mark the start of a sampling event:
In this scenario, E.G. On Monday a cycle of 8 seconds is taken at 1:00 and at 5:00, but on Tuesday there is only one sample at 3:00.
Does it make sense to concatenate several signals from different dates (nine in this case) to train the anomaly detection algorithm?
In that case, which can be proper values for the following parameters:
Outbound Anomaly Rate: To start with, it makes sense to me for this value to be 8 seconds, which is the same duration of the sampled cycles.
Sampling rate: The sampling rate of the system in production is 4 milliseconds, does this rate have to be the
