A New Evolutionary Morphological-Rank-Linear Approach for Time Series Prediction

Título: A New Evolutionary Morphological-Rank-Linear Approach for Time Series Prediction

Autores: Araújo, Ricardo de A.

Resumo: This work presents a new evolutionary morphological-rank-linear approach in order to overcome the random walk dilemma for time series prediction. The proposed approach, referred to as Evolutionary Morphological-Rank-Linear Forecasting (EMRLF) method, consists of an intelligent hybrid model composed of a Morphological-Rank-Linear (MRL) filter combined with a Modified Genetic Algorithm (MGA), which performs an evolutionary search for the minimum number of relevant time lags capable of a fine tuned characterization of the time series, as well as for the initial (sub-optimal) parameters of the MRL filter. Each individual of the MGA population is improved using the Least Mean Squares (LMS) algorithm to further adjust the parameters of the MRL filter, supplied by the MGA. After built the prediction model, the proposed method performs a behavioral statistical test with a phase fix procedure to adjust time phase distortions that can appear in the modeling of financial time series. An experimental analysis is conducted with the method using two real world stock market time series according to a group of performance metrics and the results are compared to both MultiLayer Perceptron (MLP) networks and a more advanced, previously introduced, Time-delay Added Evolutionary Forecasting (TAEF) method.

Palavras-chave: Morphological-Rank-Linear Filters; Genetic Algorithms; Intelligent Hybrid Models; Financial Time Series; Stock Market Prediction

Páginas: 7

Código DOI: 10.21528/CBRN2003-009

Artigo em PDF: 015_CBRN2009.pdf

Arquivo BibTex: 015_CBRN2009.bib