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Consider a number of timeseries. Here we have 3, just to make it dead simple. Note that they're all different lengths.

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The very first thing I do is splitting by the original sample dimension:

enter image description here

Then, to deal with the uneven lengths, I chop it into smaller windows: enter image description here

Where each window is a slice of w/h, and some spacing s to control for the amount of overlap (bonus question: is overlap a no-no?).

Then I simply take all my windows, shuffle them and voila, a dataset for any machine learning algorithm: enter image description here

Now, my question is simply: is this correct? scikit-learn prefers to do splits along the time-axis https://scikit-learn.org/stable/modules/cross_validation.html#time-series-split, which to me is

a) a bit strange, considering that each split is a different shape (unless the idea is to train a new model for each w/h shape combination? If so, cross-validation is a slightly misleading name) and

b) seems to assume that all future samples are simply continuations of pre-existing timeseries.

enter image description here

Am I correct in my assumptions, both about my own and scikit-learn's way of working?

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