REFINEMENTS TO MODEL SELECTION FOR NONLINEAR TIME SERIES
Tomomichi Nakamura, Kevin T. Judd, Alistair Mees · International Journal of Bifurcation and Chaos · 2003
Many models of the dynamics of nonlinear time series have large numbers of parameters and tend to overfit. This paper discusses algorithms for selecting the best basis functions from a dictionary for a model of a time series. Selecting the optimal subset of basis functions is typically an NP-hard problem which usually has to be solved by heuristic methods. In this paper, we propose a new heuristic that is a refinement of a previous one. We demonstrate with applications to artificial and real data. The results indicate that the method proposed in this paper is able to obtain better models in most cases.