A covariance matrix adaptation based evolutionary methodology for phase adjustment in financial time series forecasting

Authors:
Ricardo de A. Araújo;Adriano L.I. de Oliveira;Sergio C.B. Soares
Affiliations:
[gm]² Intelligent Systems, Campinas, Brazil;Rural Federal University of Pernambuco, Recife, Brazil;Federal University of Pernambuco, Recife, Brazil
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 3
Cited 1

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Abstract

In this paper we present a methodology, called covariance matrix adaptation based evolutionary (CMAbE), to solve the financial time series forecasting problem. The proposed methodology consists of a hybrid model composed of multilayer perceptrons (MLPs) combined with the Covariance Matrix Adaptation Evolution Strategy (CMAES), which determines the most fitted time lags to characterize the time series phenomenon, as well as searches for the best architecture, parameters and training algorithm of MLP networks. An experimental analysis is conducted with the proposed methodology through two real world financial time series, and the obtained results are discussed and compared to results found with recently methods presented in literature.