Hybrid differential evolutionary system for financial time series forecasting

This paper proposes a hybrid differential evolutionary system (HDES) for financial time series forecasting, which performs a differential evolutionary search for the minimum dimension to determining the characteristic phase space that generates the time series phenomenon. It consists of an intelligent hybrid model composed of an artificial neural network (ANN) combined with the improved differential evolution (IDE). The proposed IDE searches for the relevant time lags for a correct time series characterization, the number of processing units in the ANN hidden layer, the ANN training algorithm and the modeling of ANN. Initially, the proposed HDES chooses the most tuned prediction model for time series representation, thus it performs a behavioral statistical test in the attempt to adjust forecast time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed HDES using two real world financial time series and five well-known performance metrics are used to assess its performance. The obtained results are compared to time-delay added evolutionary forecasting (TAEF) method.