A multi-parametric simulation study of neural networks' performance for nonlinear data against linear regression analysis in economics
Evangelos Sambracos, Marina Maniati, Sokratis Sklavos · International Journal of Business Forecasting and Marketing Intelligence · 2020
Different mathematical and dynamic methods have been developed addressing the problem of forecasting, with the regression analysis to be one of the most frequently used statistical procedures. Meanwhile, neural networks (NNs) are considered to be well suited in finding accurate solutions in an environment characterised by volatility, noisy, irrelevant or partial information. In this chapter, a simulation study compares the performance of NNs against linear regression analysis is based on multiple combinations (421 in total) of five different factors providing those cases that the NN performs better than the LRM and defining the output bias as the main contributor to the NN outcome.