Financial time series forecasting with a bio-inspired fuzzy model

In general, times series forecasting is considered as a highly complex problem, which is particularly true for financial time series. In this paper, a fuzzy model evolved through a bio-inspired algorithm is proposed to produce accurate models for the prediction of these time series. The performance of this model is compared to that of a group of state-of-the-art statistical models. A thorough experimental study is designed and carry out in order to assess the merits of the proposal. The experimental results allow us to state that our proposal forecasts consistently outperform the other considered methods.

► We address financial time series forecasting from a Soft Computing perspective. ► Our solution is based on the evolution of FRBS through an evolutionary algorithm. ► We compare performance of our proposal to that of a number of statistical methods. ► A thorough empirical analysis has been carried out using 23 daily DJIA series. ► Statistical tests show that our proposal clearly outperforms the other methods.