# Tag Info

9

Correlation Between Entries The first recommendation is to ensure that appropriate warning and informational entries in the log file are presented along with errors into the machine learning components of the solution. All log entries are potentially useful input data if it is possible that there are correlations between informational messages, warnings, ...

4

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 ...

3

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.

2

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 ...

2

You observation is correct although the terminology needs a little explaining. The term 'out-of-distribution' (OOD) data refers to data that was collected at a different time, and possibly under different conditions or in a different environment, then the data collected to create the model. They may say that this data is from a 'different distribution'. ...

1

self is used in python class methods, to denote an instance of the class in python. For example in this snippet self.name will assign the given name to the instance. class Dog: def __init__(self, name): self.name = name Here, as you're not subclassing, you should remove the self and just use model. self.model. -> model. You'll also need to ...

1

It sounds like you only have "normal" examples with which to train your model, so this makes the problem feel like an application for outlier detection algorithms. There are a variety of approaches here. You could indeed take an autoencoder approach and then use the reconstruction error to determine if a new image is normal or not, on the ...

1

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

1

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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If you are OK to use python, thy novelty-detection with sklearn: https://scikit-learn.org/stable/modules/outlier_detection.html

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