Forecasting and dynamic modeling have common applications in science and engineering. Time series data are often
found from different sources, astrophysical, biological, economical, etc. It is then very important to predict future values
of these data series from the existing data. The immune system is a very complex system with several mechanisms to
defense against pathogenic organisms. Inspired by the principles of immune system and biology evolution, a novel
algorithm based on the combination of immune evolution and neural network is proposed to forecast nonlinear time
series, which imitates the cellular clonal selection theory of biology immune system and the mutation ideas of biology
evolution process. Then, the mutation intensity of each antibody is decided by its objective function value; similar
antibodies are suppressed by computing the affinity of antibodies and new antibodies are produced dynamically to
maintain the diversity. Application of the proposed algorithm to nonlinear time series of sunspots number modelling and
prediction is investigated. The experimental results by different methods confirm that the proposed method has better
generalization performance than that of the Fuzzy genetic algorithm (FGA), Genetic programming (GP), Automatic
Regression Model (AR) and Automatic Regression Moving Average Model (ARMA)
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