(If you happen to haven’t learn Half 1 but, test it out right here.)
Lacking information in time-series evaluation is a recurring drawback.
As we explored in Half 1, easy imputation methods and even regression-based models-linear regression, resolution timber can get us a great distance.
However what if we must deal with extra refined patterns and seize the fine-grained fluctuation within the complicated time-series information?
On this article we are going to discover Okay-Nearest Neighbors. The strengths of this mannequin embrace few assumptions as regards to nonlinear relationships in your information; therefore, it turns into a flexible and sturdy resolution for lacking information imputation.
We will probably be utilizing the identical mock vitality manufacturing dataset that you just’ve already seen in Half 1, with 10% values lacking, launched randomly.
We’ll impute lacking information in utilizing a dataset which you can simply generate your self, permitting you to observe alongside and apply the methods in real-time as you discover the method step-by-step!
