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A novel forecasting model combinating autoregressive integrating moving average(ARIMA) with dynamic e-insensitive cost function support vector machine(e-DSVM)was brought forth, which showed the complicated and dynamic characteristics of Dendrolimus punctatus occurrence. ARIMA model was used to capture the linear feature of the time series and e-DSVM model to fit the nonlinear component of the time series to obtain the ensemble forecasting result by adding ARIMA to e-DSVM. The prediction performances of the method was tested by Dendrolimus punctatus occurrence, and the results showed that the hybrid model, which took advantage of the unique strength of the two models in linear and nonlinear modeling, had better accuracy than the single model and simple ensemble forecasting model incorporating ARIMA and SVM. As a novel model combinated ARIMA with e-DSVM , the combinatining model had the advantages of structural risk minimization and non-linear characteristics, which was suitable for small samples, being able to avoid the over-fit. It is a new, powerful tool in pests forecasting work.

In order to solve the problems of high nonlinearity of regional social systems, the hybrid method of support vector machine and genetic algorithm is applied to predict regional economies. Support vector machine is a statistical learning procedure based on the structural risk minimization principle, which has very strong nonlinear regression. Genetic algorithm is well-known as an more widely used method, which is applied to select the operating parameters of support vector machine. The regional economies data from 1987 to 2003 are used to verify the effectiveness of the proposed approach. It is observed from the experiments that prediction performance of the proposed approach than that of normal support vector machine. It is concluded that the proposed approach is a viable alternative to predict regional economies.