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一种基于误差补偿的多元混沌时间序列混合预测模型

韩敏 许美玲

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一种基于误差补偿的多元混沌时间序列混合预测模型

韩敏, 许美玲

A hybrid prediction model of multivariate chaotic time series based on error correction

Han Min, Xu Mei-Ling
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  • 针对多元混沌时间序列的预测问题, 考虑到单纯改进储备池算法无法明显地提高预测精度, 提出一种基于误差补偿的时间序列混合预测模型. 实际观测的数据既包含线性特征又包含非线性特征. 首先利用自回归移动平均模型预测线性特征, 使得残差数据仅含非线性特征; 然后, 建立正则化回声状态网络模型预测; 最后, 将非线性部分的预测值与线性部分的预测值相加, 以实现高精度的多元混沌时间序列预测. 基于Lorenz和太阳黑子-黄河径流量时间序列的仿真实验验证了本文所提模型的有效性.
    Considering the problem that simply modifying the reservoir algorithm cannot significantly improve the prediction accuracy of chaotic multivariate time series, in this paper we propose a hybrid prediction model based on error correction. The observed data includes both linear and nonlinear features. First, we use autoregressive and moving average model to capture the linear features, then build a regularized echo state network to portray the dynamic nonlinear features. Finally, we add the predicted nonlinear value to the predicted linear value, in order to improve forecasting accuracy achieved by either of the models used separately. The experimental results of Lorenz and Sunspot-Runoff in the Yellow River time series demonstrate the effectiveness and characteristics of the proposed model herein.
    • 基金项目: 国家自然科学基金(批准号: 61074096)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61074096).
    [1]

    Xiu C B, Xu M 2010 Acta Phys. Sin. 59 7650 (in Chinese) [修春波, 徐勐 2010 物理学报 59 7650]

    [2]

    Zhang J S, Xiao X C 2000 Acta Phys. Sin. 49 403 (in Chinese) [张家树, 肖先赐 2000 物理学报 49 403]

    [3]

    Rojas I, Valenzuela O, Rojas F, Guillen A, Herrera L J, Pomares H, Marquez L, Pasadas M 2008 Neurocomputing 71 519

    [4]

    Chattopadhyay S, Jhajharia D, Chattopadhyay G 2011 C. R. Geosci. 343 433

    [5]

    Cao F L, Xu Z B 2004 Sci. China E 34 361 (in Chinese) [曹飞龙, 徐宗本 2004 中国科学: E辑 34 361]

    [6]

    Buonomano D V 2009 Neuron 63 423

    [7]

    Ma Q L, Zheng Q L, Peng H, Qin J W 2009 Acta Phys. Sin. 58 1410 (in Chinese) [马千里, 郑启伦, 彭宏, 覃姜维 2009 物理学报 58 1410]

    [8]

    Zhang X, Wang H L 2011 Acta Phys. Sin. 60 110201 (in Chinese) [张弦, 王宏力 2011 物理学报 60 110201]

    [9]

    Jaeger H, Haas H 2004 Science 304 78

    [10]

    Song Q S, Feng Z R, Li R H 2009 Acta Phys. Sin. 58 5057 (in Chinese) [宋青松, 冯祖仁, 李人厚 2009 物理学报 58 5057]

    [11]

    Dutoit X, Schrauwen B, Campenhout J V, Stroobandt D, Brussel H V, Nuttin M 2009 Neurocomputing 72 1534

    [12]

    Zhang G P 2003 Neurocomputing 50 159

    [13]

    Seghouane A K 2011 IEEE Trans. Aerosp. Electron. Syst. 47 1154

    [14]

    Shi Z W, Han M 2007 IEEE Trans. Neural Netw. 18 359

    [15]

    Chatzis S P, Demiris Y 2011 IEEE Trans. Neural Netw. 22 1435

  • [1]

    Xiu C B, Xu M 2010 Acta Phys. Sin. 59 7650 (in Chinese) [修春波, 徐勐 2010 物理学报 59 7650]

    [2]

    Zhang J S, Xiao X C 2000 Acta Phys. Sin. 49 403 (in Chinese) [张家树, 肖先赐 2000 物理学报 49 403]

    [3]

    Rojas I, Valenzuela O, Rojas F, Guillen A, Herrera L J, Pomares H, Marquez L, Pasadas M 2008 Neurocomputing 71 519

    [4]

    Chattopadhyay S, Jhajharia D, Chattopadhyay G 2011 C. R. Geosci. 343 433

    [5]

    Cao F L, Xu Z B 2004 Sci. China E 34 361 (in Chinese) [曹飞龙, 徐宗本 2004 中国科学: E辑 34 361]

    [6]

    Buonomano D V 2009 Neuron 63 423

    [7]

    Ma Q L, Zheng Q L, Peng H, Qin J W 2009 Acta Phys. Sin. 58 1410 (in Chinese) [马千里, 郑启伦, 彭宏, 覃姜维 2009 物理学报 58 1410]

    [8]

    Zhang X, Wang H L 2011 Acta Phys. Sin. 60 110201 (in Chinese) [张弦, 王宏力 2011 物理学报 60 110201]

    [9]

    Jaeger H, Haas H 2004 Science 304 78

    [10]

    Song Q S, Feng Z R, Li R H 2009 Acta Phys. Sin. 58 5057 (in Chinese) [宋青松, 冯祖仁, 李人厚 2009 物理学报 58 5057]

    [11]

    Dutoit X, Schrauwen B, Campenhout J V, Stroobandt D, Brussel H V, Nuttin M 2009 Neurocomputing 72 1534

    [12]

    Zhang G P 2003 Neurocomputing 50 159

    [13]

    Seghouane A K 2011 IEEE Trans. Aerosp. Electron. Syst. 47 1154

    [14]

    Shi Z W, Han M 2007 IEEE Trans. Neural Netw. 18 359

    [15]

    Chatzis S P, Demiris Y 2011 IEEE Trans. Neural Netw. 22 1435

计量
  • 文章访问数:  6003
  • PDF下载量:  790
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-11-05
  • 修回日期:  2013-02-16
  • 刊出日期:  2013-06-05

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