Nonlinear time series methods have developed rapidly over a quarter of a century and have reached an advanced state of maturity during the last decade. Implementations of these methods for experimental data are now widely accepted and fairly routine
Traditional data mining methods are designed to deal with “static” databases, i.e. databases where the ordering of records (or other database objects) has nothing to do with the patterns of interest. Though the assumption of order irrelevance may be
by Bruce Western, Meredith Kleykamp in Princeton University: Political relationships often vary over time but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change-point in a time
We discuss some problems encountered in inference of directionality of coupling, or, in the case of two interacting systems, in inference of causality from bivariate time series. We identify factors and in°uences which can lead to either decreased t
