Abstract
This paper presents a novel Dynamic Self-Organised Multilayer Neural Network that can be used for prediction of noisy time series data. The proposed technique is based on the Immune Algorithm for financial time series prediction; combining the properties of both recurrent and self-organised neural networks. The network is derived to ensure that a unique equilibrium state can be achieved to overcome the known stability and convergence problems. Extensive simulations for multi-step prediction in stationary and non-stationary time series were performed. The resulting projection made by the proposed network shows substantial profits on financial historical signals when compared to other neural network approaches. These simulations have suggested that dynamic immunology-based self-organised neural networks have a better ability to capture the chaotic movement in financial signals.