Nonlinear Modelling and Forecasting

Based on a Santa Fe Institute and NATO sponsored workshop, this book brings together the ideas of leading researchers in the rapidly expanding, interdisciplinary field of nonlinear modeling in an attempt to stimulate the cross-fertilization of ideas and the search for unifying themes. The central theme of the workshop was the construction of nonlinear models from time-series data. Approaches to this problem have drawn from the disciplines of multivariate function approximation and neural nets, dynamical systems and chaos, statistics, information theory, and control theory. Applications have been made to economics, mechanical engineering, meteorology, speech processing, biology, and fluid dynamics. }The interdisciplinary field of nonlinear modeling has grown rapidly over the last decade due to the increasing availability of computer resources, which allows for the collection of increasingly large data sets and the analysis of the data sets with numerically intensive algorithms. In addition, the field has also grown with the increasing recognition of the ubiquity and importance of the effects of nonlinear dynamics in the natural and social sciences.Based on a Santa Fe Institute and NATO sponsored workshop, this book brings together the ideas of leading researchers in this rapidly expanding, interdisciplinary field in an attempt to stimulate the cross-fertilization of ideas and the search for unifying themes. The central theme of the workshop was the construction of nonlinear models from time-series data. Approaches to this problem have drawn from the disciplines of multivariate function approximation and neural nets, dynamical systems and chaos, statistics, information theory, and control theory. Applications have been made to economics, mechanical engineering, meteorology, speech processing, biology, and fluid dynamics.The papers included discuss various approaches to nonlinear multivariate function approximation, statistical issues in time-series analysis, invariants associated with chaotic attractors, and extimation of invariants. Finally, the last seven papers discuss applications to a variety of time-series data using nonlinear modeling and forecasting ideas developed by the authors themselves. }