Overcoming the random walk dilemma using a Covariance Matrix Adaptation Evolutionary method

This paper proposes the Covariance Matrix Adaptation based Evolutionary (CMAbE) methodology to overcome the random walk dilemma, characterized by one step delay regarding the real time series values, adjusting time phase distortions in the financial time series forecasting problem. The proposed CMAbE methodology consists of a hybrid model composed of the MultiLayer Perceptron (MLP) networks and the Covariance Matrix Adaptation Evolution Strategy (CMAES), which searches for the best particular time lags to optimally describe the time series phenomenon, as well for the best architecture, parameters and training algorithm of MLP networks. An experimental analysis is conducted with the proposed methodology through four real world financial time series, and the obtained results are discussed and compared to results found with recently methods presented in literature.