Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory p...Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.71171011 and 91224001Program for New Century Excellent Talents in University(NCET-12-0756)
文摘Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.
