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January  2008, 4(1): 33-52. doi: 10.3934/jimo.2008.4.33

Knot-optimizing spline networks (KOSNETS) for nonparametric regression


School of Mathematics and Statistics, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009


CSIRO Mathematical and Information Sciences, Private Bag 5, Wembley, WA 6913, Australia


Department of Actuarial Studies, Macquarie University, Sydney, Australia

Received  July 2006 Revised  November 2007 Published  January 2008

In this paper we present a novel method for short term forecast of time series based on Knot-Optimizing Spline Networks (KOSNETS). The time series is first approximated by a nonlinear recurrent system. The resulting recurrent system is then approximated by feedforward $B$-spline networks, yielding a nonlinear optimization problem. In this optimization problem, both the knot points and the coefficients of the $B$-splines are decision variables so that the solution to the problem has both optimal coefficients and partition points. To demonstrate the usefulness and accuracy of the method, numerical simulations and tests using various model and real time series are performed. The numerical simulation results are compared with those from a well-known regression method, MARS. The comparison shows that our method outperforms MARS for nonlinear problems.
Citation: Song Wang, Quanxi Shao, Xian Zhou. Knot-optimizing spline networks (KOSNETS) for nonparametric regression. Journal of Industrial & Management Optimization, 2008, 4 (1) : 33-52. doi: 10.3934/jimo.2008.4.33

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