Using genetic algorithms for time series prediction
- Resource Type
- Conference
- Authors
- Yang, Cheng-Xiang; Zhu, Yi-Fei
- Source
- 2010 Sixth International Conference on Natural Computation Natural Computation (ICNC), 2010 Sixth International Conference on. 8:4405-4409 Aug, 2010
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
General Topics for Engineers
Time series analysis
Predictive models
Genetics
Analytical models
Optimization
Biological cells
Mathematical model
time series
genetic algorithms
nonlinear
modeling
forecasting
- Language
- ISSN
- 2157-9555
2157-9563
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.