An Online BP Learning Algorithm with Adaptive Forgetting Factors for Foreign Exchange Rates Forecasting

The foreign exchange market is a complex, evolutionary, and nonlinear dynamical system. Foreign exchange rate series as a kind of financial time series are inherently noisy, non-stationary, and deterministically chaotic (Yaser and Atiya, 1996). This means that the distribution of foreign exchange rate series changes over time. Not only is a single data series non-stationary in the sense of the mean and variance of the series, but the relationship of the data series to other related data series may also be continuously changing (Yu et al., 2006b). Modeling such dynamical and non-stationary time series is a challenging task. Over the past few years, neural networks have been successfully applied to foreign exchange rates prediction and achieve promising results, as Chapters 1 and 4 indicated.

The rest of this chapter is organized as follows. In Section 5.2, the proposed online learning algorithm with adaptive forgetting factor is first presented in terms of the gradient descent algorithm and optimization techniques. For further illustration, an empirical analysis is then given in Section 5.3. Finally, some concluding remarks are drawn in Section 5.4.