[5]黃安強(qiáng)， 肖進(jìn)， 汪壽陽(yáng). 一個(gè)基于集成情境知識(shí)的組合預(yù)測(cè)方法[J]. 系統(tǒng)工程理論與實(shí)踐， 2011， （1）：123-127.
[6]A. Azaron， C. Perkgoz， M. Sakawa. A genetic algorithm approach for the timecost tradeoff in PERT networks[J]. Applied Mathematics and Computation， 2005， 168 （2）： 1317-1339.
[7]李松， 劉力軍， 解永樂(lè). 遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的短時(shí)交通流混沌預(yù)測(cè)[J]. 控制與決策， 2011， 26（5）： 76-81.
（責(zé)任編輯：姚德權(quán)）
An Intelligent Hybrid Prediction for Financial Time Series Based on the GreyARIMA
LUO Hongben1，2
. （1.School of Business，Central South University， Changsha， Hunan 410083，China；
2.Office of SCIentific R&D Hunan University，Changsha，Hunan 410082，China）.
Abstract：An Intelligent hybrid financial time series forecasting model is proposed based on a grey ARIMA. First， the financial times series grey forecasting model is constructed， and at the same time three parameters were optimized using PSO algorithm. The grey forecasting model residuals are then analyzed with ARIMA， and the coefficients for the ARIMA model are optimized with a genetic algorithm. Finally， the predicative results of the ARIMA model are used to compensate the grey forcasting model.The empirical results show that the algorithm proposed in this paper can have better fitting precision for a period of MA<107 time series data with the prediction error controlled within 5%； compared with the grey prediction algorithm and the neural network algorithm， the algorithm has obvious advantages in terms of the mean absolute error， root mean square error and the trend prediction.
Key words：Financial Time Series； Grey Prediction； ARIMA； PSO； Genetic Algorithm
[6]A. Azaron， C. Perkgoz， M. Sakawa. A genetic algorithm approach for the timecost tradeoff in PERT networks[J]. Applied Mathematics and Computation， 2005， 168 （2）： 1317-1339.
[7]李松， 劉力軍， 解永樂(lè). 遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的短時(shí)交通流混沌預(yù)測(cè)[J]. 控制與決策， 2011， 26（5）： 76-81.
（責(zé)任編輯：姚德權(quán)）
An Intelligent Hybrid Prediction for Financial Time Series Based on the GreyARIMA
LUO Hongben1，2
. （1.School of Business，Central South University， Changsha， Hunan 410083，China；
2.Office of SCIentific R&D Hunan University，Changsha，Hunan 410082，China）.
Abstract：An Intelligent hybrid financial time series forecasting model is proposed based on a grey ARIMA. First， the financial times series grey forecasting model is constructed， and at the same time three parameters were optimized using PSO algorithm. The grey forecasting model residuals are then analyzed with ARIMA， and the coefficients for the ARIMA model are optimized with a genetic algorithm. Finally， the predicative results of the ARIMA model are used to compensate the grey forcasting model.The empirical results show that the algorithm proposed in this paper can have better fitting precision for a period of MA<107 time series data with the prediction error controlled within 5%； compared with the grey prediction algorithm and the neural network algorithm， the algorithm has obvious advantages in terms of the mean absolute error， root mean square error and the trend prediction.
Key words：Financial Time Series； Grey Prediction； ARIMA； PSO； Genetic Algorithm