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Normalization and Cost #310
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What are you using to draw these graphs as an unrelated question!
I do agree in some instances there might be a need to remove any pre processing of the data, this can be done upstream if needed unless it's an inherent part of the pelt algorithm. |
It's not inherent to the pelt algorithm I think? Unless there is some hidden pre processing going on (?). I would like to know whether I should do my own normalization up front, and how it might affect certain cost functions in the pelt algorithm (L1, L2, ...). The plotting is just matplotlib + seaborn! |
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Hi, this is a question, not an issue.
I have a bunch of features that I track over time. I am feeding them into
signal
here is (for example) a 500x16 (timepoints x features). The features themselves live on pretty different scales, such that I thought that some kind of scaling / normalization (for example via https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale) could make sense. Now I wonder though how different costs would be affected by that. In the example I am attaching below you can see the normalized signal for L1 and L2 norms -> change points are depicted with dashed lines. You can see that there are some obvious misses there (calibrating the penalty helps sometimes, but is a finicky process).Should normalization be skipped altogether / is there a better alternative cost for these kind of signals?
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