Table 3.

Common approaches to data interpolation.

Name	Summary of approach
Nearest neighbor	Assuming the value of the next-nearest sample
Mean analysis	Taking the mean value of the sample before and after the missing data point
Linear interpolation	Using the slope of a best-fit line to predict missing values, assuming a linear relationship
Cubic interpolation	Fitting short length “splines” over regions shaped using third-degree polynomials
Spline interpolation	Similar to cubic interpolation though uses short-length splines, as opposed to polynomial functions, to model missing data in a piece-wise fashion