Hi Andy,
I'll try to be more precise with the CV I'm proposing. Comparing to the
actual TimeSeriesSplit, these would be the new parameters:
-Rolling window Or Variable length window:
Rolling window mode keeps the CV-training set with the same size for all folds,
shifting forward at each iteration of CV. Variable length window mode
increments the size of CV-training set at each fold iteration (actual
implementation in TimeSeriesSplit)
________________________________
De: scikit-learn <scikit-learn-bounces+a_lago=hotmail....@python.org> en nombre
de Andreas Mueller <t3k...@gmail.com>
Enviado: viernes, 28 de abril de 2017 05:48 p. m.
Para: Scikit-learn user and developer mailing list
Asunto: Re: [scikit-learn] Contribution to sklearn: Cross validation of time
series
Hey Andres.
I think there might be a PR for that.
Can you explain the minimum size of the training set? How is that used?
I thought the other main option would be "rolling window" cross validation
to use a fixed length cv training set.
So the two options to me were rolling window and what we're doing right now.
Can you elaborate on the other use cases, like minimum size of the training set
and why you would want the other options with a variable length training set?
Thanks,
Andy
On 04/27/2017 09:44 AM, andres lago wrote:
Hello,
I'd like to contribute with a new functionality in sklearn. It's the cross
validation of time series. It's an evolution of the current functionality,
implemented by TimeSeriesSplit.
TimeSeriesSplit only allows the user to set the number of folds. In real
life, when performing the cross validation of time series, other parameters are
required, for instance:
-minimum size of CV-training set
-size of CV-test set
-fixed or variable length of CV-training set.
The functionality is inspired by the R library 'caret'.
If you agree, I can share my code. I developed it for a project with the
french rail company SNCF. It's in production now.
Regards,
Andres
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