Using genetic algorithms for time series prediction

This paper proposes using the genetic algorithms (GAs) for nonlinear time series prediction. A nesting evolution scheme is designed to evolve the forecasting models. In the outer evolution cycle, a binary-coded genetic algorithm is employed to evolve the structures of nonlinear polynomial type models. Then the coefficients of the evolved models are introduced and optimized by a real-coded genetic algorithm in the inner evolution cycle. The evolution process is repeated by using genetic operators and the principle of `survival of the fittest´ until find the satisfied results. The proposed method is applied to deformation prediction of the dangerous rock mass in rock engineering. The results indicate the applicability of the proposed algorithm with enough accuracy.