So if I understood correctly: You have data from 2 sensors in time: Ar flow and BackGas Flow (SCCM, what is that?) You have that data for multiple products. 1 - Since it is relatively low dimensional, you may try using raw data with K-Means or Self Organizing Maps. 2 - If you searching for anomalies in time, you might try using feature engineering with ...


If your anomalies are simply peaks, why should you be using machine learning methods? You could use peak detection algorithms for the purpose. If you still insist on ML, isolation forest is a good try.


First of all, you mention that you have categorical data. I don't see how you can define similarity so that you can also define the distance between the predicted value and the ground truth (error). You can do that only if the data are ordinal. If you want to just classify between normal and anomalous points (binary classification), without caring about ...


$F$ in this context is the output of the Convolutional Neural Network that's being trained, which is of the same size as $X$.


Hierarchical Temporal Memory is a model well suited for anomaly detection. It is also pretty interesting and different from currently typical Deep Learning models.

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