We propose a dynamic adaptive partitioning scheme for nonparametric analysis of stationary nonlinear time series with values in IR d (d 1). We use information from past values to construct adaptive partitioning in a dynamic fashion which is then diierent from the more common static schemes in the regression setup. The idea of dynamic partitioning is novel. We make it constructive by proposing a...

In this paper we discuss an approach for modelling nonlinear time series data based on wavelet smoothing. The technique involves decomposing the series into two components a deterministic component which when extracted by wavelet ̄ltering leaves a random component which can be easily modelled using well known linear time series modelling techniques or by a simple diagonal pure bilinear model dis...

This paper comments on the multivariate GARCH modeling of federal funds and the 3-month Treasury bill rate by Kyrtsou and Vorlow. 2008 Elsevier Inc. All rights reserved. JEL classification: G0; C4

Abstract In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. This learning process in this method is similar to conventional gradient descent learning process, except that the input patterns and parameters are stored in mem...

1 Nonlinear dynamical analysis of noisy time series.