A neural gas mixture autoregressive network for modelling and forecasting FX time series

Nowadays, there exist various methods for modelling and forecasting foreign exchange (FX) rates including economical models, statistical methods and learning neural networks. Dealing with the problems of nonstationarity and nonlinearity has been a challenge. In this paper, we propose a combined neural model for effectively tackling the problems. The model is termed as neural gas mixture of autoregressive models (NGMAR) and it organises the mixture of autoregressive models in the way of the neural gas. By taking the advantages of dynamic neighbourhood rankings of neural gas and the more appropriate similarity measure of the sum of autocorrelation coefficients, the model is able to effectively model and forecast nonstationary and nonlinear time series. The NGMAR has been tested on several benchmark data sets as well as a variety of FX rates. The experimental results show that the proposed method outperforms significantly other methods, in terms of normalised root mean squared error and correct trend prediction percentage.