  Regression of data generated in simulations or experiments has important
implications in sensitivity studies, uncertainty analysis, and prediction
accuracy. Depending on the nature of the physical model, data points may not be
evenly distributed. It is not often practical to choose all points for
regression of a model because it doesn't always guarantee a better fit. Fitness
of the model is highly dependent on the number of data points and the
distribution of the data along the curve. In this study, the effect of the
number of points selected for regression is investigated and various schemes
aimed to process regression data points are explored. Time series data i.e.,
output varying with time, is our prime interest mainly the temperature profile
from enhanced geothermal system. The objective of the research is to find a
better scheme for choosing a fraction of data points from the entire set to
find a better fitness of the model without losing any features or trends in the
data. A workflow is provided to summarize the entire protocol of data
preprocessing, regression of mathematical model using training data, model
testing, and error analysis. Six different schemes are developed to process
data by setting criteria such as equal spacing along axes (X and Y), equal
distance between two consecutive points on the curve, constraint in the angle
of curvature, etc. As an example for the application of the proposed schemes, 1
to 20% of the data generated from the temperature change of a typical
geothermal system is chosen from a total of 9939 points. It is shown that the
number of data points, to a degree, has negligible effect on the fitted model
depending on the scheme. The proposed data processing schemes are ranked in
terms of R2 and NRMSE values.
