David A. Vaccari
ResearcherSchool of Engineering & Science
We are developing software, which we call TaylorFit, that can produce models with the prediction performance of Artificial Neural Networks (ANNs) with Multivariate Polynomial Regression (MPR) for Response Surface Analysis (RSA). In comparison to linear methods, MPR shares with ANNs the ability to produce models that are much more accurate and unbiased. But MPR models will be more useful than ANNs because they are transparent, tractable, and transportable, in addition to being more compact and relatively resistant to overfitting. Multivariate polynomials are much easier to manipulate, incorporate into other software, publish, plot, etc.
MPR models can also replace linear time-series modeling techniques such as ARMA (Box- Jenkins) models and can produce far superior results because they incorporate nonlinear effects. In these applications, they represent a form of what are known as Nonlinear Autoregressive Moving Average with eXogenous variables (NARMAX) models (although without the “MA” part).
TaylorFit runs client-side within a browser. We have made it available for free online at www.TaylorFit-RSA.com. Applications include business and finance, health and science, and almost any area where multivariate numerical data are collected with cause-and-effect relationships expected.
“Data is expensive, computations are cheap.” We recommend that those who have analyzed data using linear methods or ANNs should consider re-analyzing their data using MPR. TaylorFit software makes it easy to quickly explore the parameter space and examine many kinds of complex relationships. Potential users can go to www.TaylorFit-RSA.com to download the Users’ Manual and start fitting models to their data.
