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I’m interested in predict anomalies in a process that works 24/7, but for training, I only have data of two days for each week, as depicts in the image below:
Considering that I have several months of data and the timestamps for each sample, is this data enough to properly train an anomaly detection model?
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The typical suggestion for training is to have 5 full cycles of the data to obtain a good training set.
Details on this can be found in this Knowledge Article: https://www.ptc.com/en/support/article/CS248761
Should be note, Anomaly Detection has a max period of 23 hours and 59 Minutes for its monitoring, and is not capable of multi day monitoring at this time.
Regards,
Neel
Thank you for posting your question to the PTC Community.
The typical suggestion for training is to have 5 full cycles of the data to obtain a good training set.
Details on this can be found in this Knowledge Article: https://www.ptc.com/en/support/article/CS248761
Should be note, Anomaly Detection has a max period of 23 hours and 59 Minutes for its monitoring, and is not capable of multi day monitoring at this time.
Regards,
Neel
@nsampat Regarding the max period of 24 hours does that mean that the sum of all 5 full cycles must be less than 24 hours, or that each individual cycle must be of 23h59m at much?
The 5 cycles must be within the 23 hours and 59 minutes, otherwise its considered a new training window.
Ideally you would want your cycles to be periodic and cyclical, Anomaly Detection does not handle acyclical data very well.
Regards,
Neel
Hi @LR_9796586
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--Sharon