Paper Downloads

P.E. McSharry and L.A. Smith (1999) Just do it! Reductionism, Modelling and Black-box Forecasting In International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling: Theory and Applications with Time-Series Prediction Competition, edited by J.A.K. Suykens and J. Vandewalle, pp. 106-111, K.U. Leuven, Belgium, Kluwer Academic Publishers.

Abstract

A new approach to nonlinear modeling is presented which, by incorporating the global behavior of the model, lifts shortcomings of both least squares and total least squares parameter estimates. Although ubiquitous in practice, a least squares approach is fundamentally flawed in that it assumes independent, normally distributed (IND) forecast errors: nonlinear models will not yield IND errors even if the noise is IND. A new cost function is obtained via the maximum likelihood principle; superior results are illustrated both for small data sets and infinitely long data streams.