基于灰色-ARIMA的金融时间序列智能混合预测研究
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引用本文:罗洪奔.基于灰色-ARIMA的金融时间序列智能混合预测研究[J].财经理论与实践,2014,(2):27-34
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作者单位
罗洪奔 (1.中南大学 商学院湖南 长沙410083
2.湖南大学 科学技术研究院,湖南 长沙410082) 
中文摘要:提出了一种基于灰色-ARIMA的金融时间序列智能混合预测模型。首先建立金融时间序列灰色预测模型,并采用PSO算法对灰色模型的三个参数进行优化;利用ARIMA算法对预测模型的残差进行分析,同时采用遗传算法对ARIMA的系数进行优化;最后用ARIMA的残差预测结果对灰色预测模型进行补偿。结果表明,以较好的精度拟合一段时期内MA<107的时间序列,预测误差控制在5%以上,与单纯的灰色预测算法和神经网络算法相比,在平均绝对误差、均方根误差和趋势准确率三项评价指标上,具有明显优势。
中文关键词:金融时间序列  灰色预测  ARIMA  PSO  遗传算法
 
An Intelligent Hybrid Prediction for Financial Time Series Based on the Grey-ARIMA
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.
keywords:Financial Time Series  Grey Prediction  ARIMA  PSO  Genetic Algorithm
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