A STOCHASTIC NEURAL NETWORK FOR FORECASTING FINANCIAL CHAOTIC TIME SERIES: NEW APPROACH

Nonlinear modeling with neural networks offers a promising approach for studying the prediction of a chaotic time series. In this paper, we propose a stochastic neural net based on Extended Kalman Filter to examine the nonlinear dynamic proprieties of some financial time series in order to differentiate between low-dimensional chaos and stochastic behavior. Kalman filtering, because it can deal with varying unobservable states, provides an efficient framework to model these non-stationary exposures.A controlled simulation experiment is used to introduce the issues involved and to present the proposed approach. Measures of forecast accurency are developed. The pertinence of this model is discussed in the light of some real word examples from the Tunisian Stock Exchange database.