Optimal parameters of the SVM for temperature prediction

Abstract. This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA). And the parameters of support vector machine (SVM) were optimized with genetic algorithm (GA), particle swarm optimization (PSO) and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.