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基于时空Kriging方法的时空数据插值研究

许美玲 邢通 韩敏

许美玲, 邢通, 韩敏. 基于时空Kriging方法的时空数据插值研究. 自动化学报, 2020, 46(8): 1681−1688 doi: 10.16383/j.aas.2018.c170525
引用本文: 许美玲, 邢通, 韩敏. 基于时空Kriging方法的时空数据插值研究. 自动化学报, 2020, 46(8): 1681−1688 doi: 10.16383/j.aas.2018.c170525
Xu Mei-Ling, Xing Tong, Han Min. Spatial-temporal data interpolation based on spatial-temporal Kriging method. Acta Automatica Sinica, 2020, 46(8): 1681−1688 doi: 10.16383/j.aas.2018.c170525
Citation: Xu Mei-Ling, Xing Tong, Han Min. Spatial-temporal data interpolation based on spatial-temporal Kriging method. Acta Automatica Sinica, 2020, 46(8): 1681−1688 doi: 10.16383/j.aas.2018.c170525

基于时空Kriging方法的时空数据插值研究

doi: 10.16383/j.aas.2018.c170525
基金项目: 

国家自然科学基金 61702077

国家自然科学基金 61773087

国家自然科学基金 61374154

中央高校基本科研业务费 DUT16RC(3)123

详细信息
    作者简介:

    许美玲  大连理工大学电子信息与电气工程学部讲师.主要研究方向为神经网络和多元时间序列预测. E-mail: xuml@dlut.edu.cn

    邢通  大连理工大学电子信息与电气工程学部硕士研究生.主要研究方向为神经网络和时空序列预测. E-mail: xt1386@mail.dlut.edu.cn

    通讯作者:

    韩敏  大连理工大学电子信息与电气工程学部教授.主要研究方向为模式识别, 复杂系统建模与分析及时间序列预测.本文通信作者. E-mail: minhan@dlut.edu.cn

Spatial-temporal Data Interpolation Based on Spatial-temporal Kriging Method

Funds: 

National Natural Science Foundation of China 61702077

National Natural Science Foundation of China 61773087

National Natural Science Foundation of China 61374154

Fundamental Research Funds for the Central Universities DUT16RC(3)123

More Information
    Author Bio:

    XU Mei-Ling Lecturer at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers neural networks and multivariate time series prediction

    XING Tong Master student at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His research interest covers neural networks and spatio-temporal series prediction

    Corresponding author: HAN Min Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers pattern recognition, modeling and analysis of complex system, and time series prediction. Corresponding author of this paper
  • 摘要: 在对气象数据进行插值的过程中, 如果只考虑数据的空间信息而忽视数据在时间上的关联, 必然影响插值的精度.针对具有时空特性的气象数据, 提出一种将时空Kriging方法与弹性网方法相结合的新方法.该方法主要利用弹性网算法解决时空Kriging算法中的时空变异函数矩阵为病态矩阵而无法求逆的问题, 通过弹性网算法获得变异函数矩阵方程的稀疏解, 从而提高时空插值的精度.在实际观测的气温数据和AQI数据上的仿真实验验证了该方法对气象时空数据插值的准确性.
  • 图  1  气温数据空间变异函数

    Fig.  1  Spatial variation function of temperature data

    图  2  气温数据时间变异函数

    Fig.  2  Temporal variation function of temperature data

    图  3  气温时空变异函数值

    Fig.  3  Spatial temporal variation function of temperature

    图  4  STKriging对射阳市气温数据插值效果

    Fig.  4  Interpolation curves of STKriging on temperature data of Sheyang City

    图  5  AQI数据时间变异函数

    Fig.  5  Temporal variation function of AQI data

    图  6  AQI数据空间变异函数

    Fig.  6  Spatial variation function of AQI data

    图  7  AQI时空变异函数值

    Fig.  7  Spatial temporal variation function of AQI

    图  8  STKriging对青岛市AQI数据插值效果

    Fig.  8  Interpolation curves of STKriging on AQI data of Qingdao City

    表  1  气温数据仿真结果比较

    Table  1  Experimental results for temperature data

    方法 RMSE NRMSE SMAPE
    [9] 1.8411 0.8605 0.0722
    STARMA[29] 1.8601 0.7062 0.0867
    STESN 2.2912 0.8008 0.5212
    ESGP[30] 3.1639 0.8585 0.0554
    STKriging 1.4765 0.6901 0.0544
    下载: 导出CSV

    表  2  AQI数据仿真结果比较

    Table  2  Experimental results for AQI data

    方法 RMSE NRMSE SMAPE
    Kriging[9] 54.3776 1.2586 0.3769
    STARMA[29] 30.2380 0.9616 0.3426
    STESN 36.9284 0.7584 0.2159
    ESGP[30] 42.1505 0.9257 0.3227
    STKriging 24.1065 0.5580 0.2143
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-09-14
  • 录用日期:  2018-01-29
  • 刊出日期:  2020-08-26

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