Predicting financial time series data using artificial immune system-inspired neural networks

This paper investigates a set of approaches for the prediction of noisy time series data; specifically, the prediction of financial signals. A novel dynamic self-organised multilayer neural network based on the immune algorithm for financial time series prediction is presented, combining the properties of both recurrent and self-organised neural networks. In an attempt to overcome inherent stability and convergence problems, the network is derived to ensure that it reaches a unique equilibrium state. The accuracy of the comparative evaluation is enhanced in terms of profit earning; empirical testing used in this work includes normalised mean square error NMSE to evaluate forecast fitness and also evaluates predictions against financial metrics to assess profit generation. Extensive simulations for multi-step prediction in stationary and non-stationary time series were performed. The resulting forecast made by the proposed network shows substantial profits on financial historical signals when compared to various solely neural network approaches. These simulations suggest that dynamic immunology-based self-organised neural networks have a better ability to capture the chaotic movement in financial signals.