Need machine learning algorithm to fill in time-series data
You can approximate a time series with a polynomial of degree n_degree by using ridge regression. You can try different degree numbers (e.g. [2,3,4,5,6]) and choose the best one. Keep in mind that higher degree models, always get lower error values. So you should somehow penalize higher degrees. 

from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

for count, degree in enumerate([2, 3, 4, 5, 6]):
    model = make_pipeline(PolynomialFeatures(degree), Ridge())
    model.fit(Time, y)


More details here.