A Comparison of Two Scaling Techniques to Reduce Uncertainty in Predictive Models

Abstract

This research examines the use of two scaling techniques to accurately transfer information from small-scale data to large-scale predictions in a handful of nonlinear functions. The two techniques are (1) using random draws from distributions that represent smaller time scales and (2) using a single draw from a distribution representing the mean over all time represented by the model. This research used simulation to create the underlying distributions for the variable and parameters of the chosen functions which were then scaled accordingly. Once scaled, the variable and parameters were plugged into our chosen functions to give an output value. Using simulation, output distributions were created for each combination of scaling technique, underlying distribution, variable bounds, and parameter bounds. These distributions were then compared using a variety of statistical tests, measures, and graphical plots.