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Interpolation of missing data is a very important feature to implement. My suggestion is that we start out with a simple interpolation mechanism, and then open up to more advanced implementations as alternatives.
@Doktorandahl will be a key contributor to future more advanced kinds of interpolation.
An interpolation is defined as taking a series:
0, NaN, 1, 0 ,0
And replacing the NaN(s) with meaningful values to allow us to use the whole series.
I might implement a very naïve interpolator as a proof of concept that we can expand upon.
The text was updated successfully, but these errors were encountered:
Thanks, I agree that this is super important. As soon as I have cleared out a few papers on my desk, I will start the process of evaluating imputation routines.
Interpolation of missing data is a very important feature to implement. My suggestion is that we start out with a simple interpolation mechanism, and then open up to more advanced implementations as alternatives.
@Doktorandahl will be a key contributor to future more advanced kinds of interpolation.
An interpolation is defined as taking a series:
And replacing the NaN(s) with meaningful values to allow us to use the whole series.
I might implement a very naïve interpolator as a proof of concept that we can expand upon.
The text was updated successfully, but these errors were encountered: