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I have 100 time series (with 200 instances each) datasets each corresponding to a particular activity. I want to perform supervised classification for the activity. I want to use time domain (time-invariant) features for each time series to perform the classification.

Example dataset

| Time | Column A | Column B |
|------|----------|----------|
| 1    | 19.45    | 0.32     |
| 2    | 22.5     | 0.89     |
| ...  | ...      | ..       |
| 200  | 33.11    | 1.23     |

100 such files.

After extracting time-invariant features from each time series I get 100 s rows as below.

| Column A minimum | Column A mean | Column B minimum | Column B mean | Label |
|------------------|---------------|------------------|---------------|-------|
| ...              | ...           | ...              | ...           | Push  |
| ...              | ...           | ...              | ...           | Pull  |

I have 100 rows (1 row corresponding to each dataset originally) in this new dataset.

Now I perform classification on this generated dataset. My question is, if I want to normalize/standardize the dataset, which would be a better way? Scaling before the extraction of time-invariant features or after generating the time domain features?

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My instincts say to normalize after extracting the time invariant features. Generally the purpose of normalization is to comparably scale different input values for the benefit of (or requirements of) a particular learning algorithm (or feature selection/dimensionality reduction). Also, in your particular case, if by “scaling before extracted features” you mean scaling the individual time series, the “min” and “max” features will most likely (depending on your choice of scaler) be “0” and “1” respectively (for each time series), so that feature would be useless.

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  • $\begingroup$ Yeah and the standard deviation would be 1! Thank you for the answer. $\endgroup$
    – Coderhhz
    Jun 17 '19 at 22:22

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