Multipredictor Modelling With Application To Chaotic Signals

This paper investigates the use of MultiPredictor Models (MPM) in the time series modelling of chaotic signals. The relation between MPM and Iterated Function Systems (IFS) coupled with the ability of IFS to generate chaotic systems motivates this approach. Emphasis is placed on two forms of MPM. The first MPM models the chaotic dynamic by way of a codebook of predictors, with both linear and nonlinear predictors discussed. We show how a dynamic neighbourhood function can be used to improve this modelling. The second MPM can be interpreted as a predictive extension of a hidden Markov model and directly parameterised by a segmental k-means algorithm. Comment is made on the forms of dynamical system for which these models are best suited. 1. INTRODUCTION The class of models refered to here as MultiPredictor Models (MPM) has received much interest recently, particularly from the speech community. For the purposes of the paper we mean by a MPM any model whose main element comprises of a ..