Neural network models as an alternative to regression

The results of this study indicate that neural network models perform best under conditions of high noise and low sample size, and with less noise or larger sample sizes, they become less competitive.

Abstract

Neural networks can provide several advantages over conventional regression models. They are claimed to possess the property to learn from a set of data without the need for a full specification of the decision model; they are believed to automatically provide any needed data transformations. They are also claimed to be able to see through noise and distortion. An empirical study evaluating the performance of neural network models on data generated from three known regression models is presented. The results of this study indicate that neural network models perform best under conditions of high noise and low sample size. With less noise or larger sample sizes, they become less competitive. However, in two of the three cases, the neural network models were able to maintain mean absolute percentage errors (MAPE) within 2% of those of the true model.>