马尔科夫模型改进的时间序列预测算法研究
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摘要
时间序列的传统预测方法能够很好地拟合和预测平稳时间序列,对于非线性非平稳的时间序列数据预测效果不好。为解决该问题,文本提出一种改进的预测算法。通过小波分解和单边重构,原始时间序列被分解为一列低频数据和两列高频数据。低频数据采用传统的时间序列方法 GARCH模型预测,高频数据使用改进方法预测。通过马尔科夫模型预测出状态区间,结合指数平滑法,预测出高频结果。与低频数据结果叠加得到最终预测结果。经误差比较,改进算法预测精度有较大提升。
The traditional time series prediction algorithm can well simulate and predict the stable time series data,but not so well to the series of nonlinear and non-stationary.To solve this problem,an improved algorithm comes up.Through the wavelet decomposition and single reconstruction,the original time series is decomposed into a layer of low frequency data and two layers of high frequency data.The GARCH model is used to forcast the low frequency data,the improved algorithm is used to forecast the two layers of high frequency data.Through Markov model predicting the state interval,with the smoothing coefficient,the high frequency data is predicted.The final forecasting result comes from the superposition of the three layers of prediction result.Through the error test,the accuracy of the improved algorithm has a major improvement.
The traditional time series prediction algorithm can well simulate and predict the stable time series data,but not so well to the series of nonlinear and non-stationary.To solve this problem,an improved algorithm comes up.Through the wavelet decomposition and single reconstruction,the original time series is decomposed into a layer of low frequency data and two layers of high frequency data.The GARCH model is used to forcast the low frequency data,the improved algorithm is used to forecast the two layers of high frequency data.Through Markov model predicting the state interval,with the smoothing coefficient,the high frequency data is predicted.The final forecasting result comes from the superposition of the three layers of prediction result.Through the error test,the accuracy of the improved algorithm has a major improvement.
引文
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[10]李慧君,杨继明,邓彤天,等.基于小波分析的汽轮机振动预测研究[J].计算机工程与应用,2014,50(12):263-270.
[11]牛丽肖,王正方,臧传治,等.一种基于小波变换和ARIMA的短期电价混合预测模型[J].计算机应用研究,2014,31(3):688-691.
[12]赵建忠,徐廷学,李海军,等.基于小波分析的导弹装备备件需求组合预测[J].电子学报,2014,42(3):417-423.
[13]邹连龙,丛明,何一冉.基于灰色-马尔科夫理论的加工误差预测[J].组合机床与自动化加工技术,2014,1(1):14-19.
[14]苏畅,周世万,尚凤军.网络中马尔科夫预测的只能丢帧实时传输方案[J].计算机应用与软件,2014,31(5):210-214.
[2]Abry P,Veitch D,Flandrin P.Long-range dependence:Revisiting aggregation with wavelets[J].Journal of Time Series Analysis,1998,19(3):253-266.
[3]Yu I,Kim C,Song Y H.A novel short-term load forecasting technique using wavelet transform analysis[J].Electric Machines and Power Systems,2000,28(6):537-549.
[4]Chui C K.An Introduction to Wavelets[M].New York:Academic Press,1992.
[5]Cryer J D,Chan K S.时间序列分析及应用:R语言[M].潘红宇译.北京:机械工业出版社,2011.
[6]易丹辉.时间序列分析:方法与应用[M].北京:中国人民大学出版社,2011.
[7]代成龙,皮德常.基于小波与灰色方法的半球谐振陀螺寿命预测[J].计算机工程,2014,40(7):291-295.
[8]黎志勇,李宁.基于小波的非平稳时间序预测方法研究[J].计算机工程与应用,2014,50(10):38-43.
[9]崔兆顺.基于小波变换的网络流量组合预测模型[J].计算机工程与应用,2014,50(10):92-100.
[10]李慧君,杨继明,邓彤天,等.基于小波分析的汽轮机振动预测研究[J].计算机工程与应用,2014,50(12):263-270.
[11]牛丽肖,王正方,臧传治,等.一种基于小波变换和ARIMA的短期电价混合预测模型[J].计算机应用研究,2014,31(3):688-691.
[12]赵建忠,徐廷学,李海军,等.基于小波分析的导弹装备备件需求组合预测[J].电子学报,2014,42(3):417-423.
[13]邹连龙,丛明,何一冉.基于灰色-马尔科夫理论的加工误差预测[J].组合机床与自动化加工技术,2014,1(1):14-19.
[14]苏畅,周世万,尚凤军.网络中马尔科夫预测的只能丢帧实时传输方案[J].计算机应用与软件,2014,31(5):210-214.