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I've asked a similar question about Gradient Boosting Machines already some time ago. This time, I would like to perform time series classifications with a transformer model. I found this Keras tutorial, which links to a tutorial about basic steps for time series classification. In there, I read the following:

Our timeseries are already in a single length (500). However, their values are usually in various ranges. This is not ideal for a neural network; in general we should seek to make the input values normalized. For this specific dataset, the data is already z-normalized: each timeseries sample has a mean equal to zero and a standard deviation equal to one. This type of normalization is very common for timeseries classification problems, see Bagnall et al. (2016).

I'm using Python and my question is: Does the StandardScaler of sklearn perform z-normalization in the sense of the Keras tutorial or should I use any other function to properly scale my data?

Your answers are highly appreciated.

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