A New Evolutionary Approach for Time Series Forecasting

This work introduces a new method for time series prediction - time-delay added evolutionary forecasting (TAEF) - that carries out an evolutionary search of the minimum necessary time lags embedded in the problem for determining the phase space that generates the time series. The method proposed consists of a hybrid model composed of an artificial neural network (ANN) combined with a modified genetic algorithm (GA) that is capable to evolve the complete network architecture and parameters, its training algorithm and the necessary time lags to represent the series. Initially, the TAEF method finds the most fitted predictor model and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of sonic series. An experimental investigation is conducted with the method with sonic relevant time series and the results achieved are discussed and coin pared, according to several performance measures, to results found with the multilayer perteptron networks and other works reported in the literature

[1]  D. Murray.  Forecasting a chaotic time series using an improved metric for embedding space , 1993 .

[2]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[3]  Efstratios F. Georgopoulos,et al.  Exchange-Rates Forecasting: A Hybrid Algorithm Based on Genetically Optimized Adaptive Neural Networks , 2002 .

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  Renate Sitte,et al.  Neural Networks Approach to the Random Walk Dilemma of Financial Time Series , 2002, Applied Intelligence.

[6]  M. Matilla-García *,et al.  A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market , 2005 .

[7]  Michael P. Clements,et al.  On the limitations of comparing mean square forecast errors , 1993 .

[8]  Garrison W. Greenwood,et al.  Evolving artificial neural network structures: experimental results for biologically-inspired adaptive mutations , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  N. Tanaka,et al.  Estimating the active demension of the dynamics in a time series based on an information criterion , 2001 .

[11]  Tiago Alessandro Espínola Ferreira,et al.  A hybrid intelligent system approach for improving the prediction of real world time series , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  H. V. Dijk,et al.  Combined forecasts from linear and nonlinear time series models , 1999 .

[13]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[14]  T. Rao,et al.  An Introduction to Bispectral Analysis and Bilinear Time Series Models , 1984 .

[15]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[16]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[17]  Carsten Peterson,et al.  Finding the Embedding Dimension and Variable Dependencies in Time Series , 1994, Neural Computation.

[18]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[19]  Xin Yao,et al.  A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.

[20]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .