Forecasting Financial Time Series Using a Hybrid Self-Organising Neural Model

Recently, various variants of the self-organsing map (SOM) have been proposed for modelling and predicting time series. However, most of them are based on lattice structure. In this paper, a hybrid neural model combining neural gas (NG) and mixture autoregressive models is developed for forecasting foreign exchange (FX) rates. It takes advantage of some NG features (i.e. neighbourhood rankings) and incorporates mixture autoregressive models, for effectively modelling and forecasting non-stationary and nonlinear time series. Experiments on FX rates are presented and the results show that the proposed model performs significantly better than other methods, in terms of normalised root-mean-squared-error and correct trend prediction percentage.