A unique technique based on chaos theory and artificial neural networks (ANN) is developed to analyse and forecast chaotic time series. An embedding theorem is used to determine the embedding parameters. Accordingly the chaotic time series is reconstructed into phase space points. Based on chaos...

The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as...

In this paper we attempt to predict the direction of change of the S&P500 index over the period 8 April 1998 to 5 February 2002 by means of a recurrent neural network (RNN). We demonstrate that the incorporation in the trading rule of the Chicago Board Options Exchange (CBOE) volatility index...

In this paper, we investigate the global robust stability for uncertain stochastic neural networks with unbounded time-varying delays and norm-bounded parameter uncertainties. A new concept of global robust μ-stability in the mean square for neural networks is given first, then by means of the...

The application of this work is to decision taking with financial time-series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing...

This paper is concerned with the robust stability analysis problem for uncertain stochastic neural networks with time-varying delay. The parameter uncertainties are assumed to be norm bounded. By defining a new Lyapunov–Krasovskii functional, the restrictions such as the time-varying delay was...