关键词: 时间序列预测, 最小二乘支持向量机, 文化基因算法, 能耗预测

Abstract: Aiming at the problem that the parameters of Least Squares Support Vector Machines（LS-SVM） are uncertain in time series prediction, this paper utilizes immune clonal memetic algorithm which adopts the advantage of global search and local search to optimize the parameters of LS-SVM. Simulation results of Lorenz time sequence prediction and building energy consumption prediction show that the prediction accuracy of this optimization method is higher than genetic algorithm and grid search algorithm, and the comparison shows that the optimized LS-SVM produces better results than Support Vector Machines（SVM） and BP neural network.

Key words: time series prediction, Least Squares Support Vector Machines（LS-SVM）, memetic algorithm, energy prediction