PCA with several time series as features of one instance with sklearn

I want to apply PCA on a data set where I have 20 time series as features for one instance. I have some 1000 instances of this kind and am looking for a way to reduce dimensionality. For every instance I have a pandas Data Frame, like:

import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.random.normal(0, 1, (300, 20)))

Is there a way to use sklearn.fit on all instances with each having a set of time series as feature space. I mean I could apply sklearn.fit on all instances separatly, but I want the same principal components for all.

Is there a way? The only not satisfying idea I have by now is to append all those series of one instance to one, so that I have one time series for one instance.

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PCA with several time series as features of one instance with sklearn 2019-03-18T03:18:00-07:00 Rating: 4.5 Diposkan Oleh: Admin