基于奇异谱分析的空间环境数据插补方法

刘帅; 李智; 龚建村; 林瑞淋; 马志昊

doi:10.13700/j.bh.1001-5965.2015.0554

基于奇异谱分析的空间环境数据插补方法

doi: 10.13700/j.bh.1001-5965.2015.0554

1.
装备学院航天指挥系, 北京 101416
2. 中国科学院国家空间科学中心, 北京 100190

基金项目: 教育部新世纪优秀人才支持计划;中国科学院青年创新促进会(Y52133A23S)

详细信息

作者简介:
刘帅男,博士研究生。主要研究方向:空间环境数据处理与建模、智能算法。 Tel.: 010-66364774 E-mail: liushuai00@sina.com;李智男,博士,教授。主要研究方向:空间信息系统综合集成、空间态势感知理论。 Tel.: 010-66364171 E-mail: lizhizys@263.net;龚建村男,博士,研究员。主要研究方向:空间环境建模与应用。 Tel.: 010-62586422 E-mail: gongjc@nssc.ac.cn;林瑞淋男,博士,副研究员。主要研究方向:空间环境建模与预报。 Tel.: 010-62586419 E-mail: rllin04@163.com;马志昊男,博士,讲师。主要研究方向:空间环境可视化、空间态势感知理论。 Tel.: 010-66364229 E-mail: snake77@163.com

通讯作者:
李智, Tel.: 010-66364171 E-mail: lizhizys@263.net

中图分类号: TP391;P353
计量
- 文章访问数: 948
- HTML全文浏览量: 102
- PDF下载量: 952
- 被引次数: 0
出版历程
- 收稿日期: 2015-08-28
- 修回日期: 2015-09-30
- 网络出版日期: 2016-04-20

Gap filling method for space environment data based on singular spectrum analysis

1.
Department of Space Command, Equipment Academy, Beijing 101416, China
2. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

Funds: Program for New Century Excellent Talents in University of Ministry of Education of China;Youth Innovation Promotion Association, Chinese Academy of Sciences(Y52133A23S)

摘要

摘要: 空间环境数据具有典型的非线性、非平稳特征,并经常包含有缺失数据,给预报模型的建立、预测以及物理过程的分析带来了一定的困难。为了实现对缺失数据的插补,基于奇异谱分析(SSA)迭代插补的思想,设计了一种能够适用于不同缺失数据分布的插补方案。该方案提取出原始时间序列中缺失数据分布数组,利用缺失数据分布数组生成交叉验证所用的测试数据集,并利用离散粒子群优化算法寻找SSA的2个关键性参数,即嵌入窗口长度和主成分个数。通过不同太阳活动年份实际观测的太阳风参数、地磁指数等实例验证了算法的有效性。
- 奇异谱分析(SSA) /
- 离散粒子群优化算法 /
- 数据插补 /
- 空间环境 /
- 时间序列
Abstract: The space environment data is known to be nonlinear and non-stationary and often contains missing values, which brings great challenge to the model-building procedures, predictions and posterior analysis. To fill the data gaps, a new gap filling method based on the iterative singular spectrum analysis (SSA) algorithm was put forward. The new method considered the distribution of missing values by extracting a distribution array first and used the array to generate the test data set. The discrete particle swarm optimization algorithm was adapted to obtain the two key parameters of SSA, i.e. the embedded window size and the number of principal components. Taking the solar wind parameters and geomagnetic indices of different solar activity years as examples, the test results demonstrate that the filling method is effective.
- singular spectrum analysis (SSA) /
- discrete particle swarm optimization algorithm /
- gap filling /
- space environment /
- time series

HTML全文

参考文献(24)

[1]	BROOMHEAD D S, KING G P.Extracting qualitative dynamics from experimental data[J].Physica D,1986,20(2-3):217- 236.
[2]	GHIL M, ALLEN R M,DETTINGER M D,et al.Advanced spectral methods for climatic time series[J].Reviews of Geophysics,2002:40(1):3.1-3.41.
[3]	ZHIGLJAVSKY A. Singular spectrum analysis for time series: Introduction to this special issue[J].Statistics and Its Interface,2010,3(3):255-258.
[4]	SCHOELLHAMER D H. Singular spectrum analysis for time series with missing data[J].Geophysical Research Letters,2001,28(16):3187-3190.
[5]	SHEN Y, PENG F,LI B.Improved singular spectrum analysis for time series with missing data[J].Nonlinear Processes in Geophysics,2014,1(2):1947-1966.
[6]	GOLYANDINA N, OSIPOV E.The “Caterpillar”-SSA method for analysis of time series with missing values[J].Journal of Statistical Planning and Inference,2007,137(8):2642-2653.
[7]	KONDRASHOV D, GHIL M.Spatio-temporal filling of missing points in geophysical data sets[J].Nonlinear Processes in Geophysics,2006,13(2):151-159.
[8]	BECKERS J M, RIXEN M.EOF calculations and data filling from incomplete oceanographic datasets[J].Journal of Atmospheric and Oceanic Technology,2003,20(12):1839-1856.
[9]	KONDRASHOV D, SHPRITS Y,GHIL M.Gap filling of solar wind data by singular spectrum analysis[J].Geophysical Research Letters,2010,37(15):1-6.
[10]	KONDRASHOV D, DENTON R,SHPRITS Y Y,et al.Reconstruction of gaps in the past history of solar wind parameters[J].Geophysical Research Letters,2014,41(8):2702-2707.
[11]	WANG H Z, ZHANG R,LI W.Improved interpolation method based on singular spectrum analysis iteration and its application in missing data recovery[J].Applied Mathematics and Mechanics,2008,29(10):1227-1236.
[12]	LIU W,JIN W D, WANG H Z,et al.An optimized atmospheric missed data recovery algorithm based on singular spectrum iteration[C]//Proceedings of IEEE Conference on Cybernetics and Intelligent.Piscataway,NJ:IEEE Press,2008:1129-1132.
[13]	GOLYANDINA N, NEKRUTKIN V,ZHIGLJAVSKY A.Analysis of structure of time series: SSA and related techniques[M].Boca Raton:The Chemical Rubber Company Press,2001:30-73.
[14]	CHENG D. Time series decomposition using singular spectrum analysis[D].Tennessee:East Tennessee State University,2014:12-14.
[15]	CLAESSEN D, GROTH A.A beginner's guide to SSA[EB/OL].Paris:Ecole Normale Supérieure,2010[2015-05-10].
[16]	WANG R, MA H,LIU G,et al.Selection of window length for singular spectrum analysis[J].Journal of the Franklin Institute,2015,352(2):1541-1560.
[17]	GOLYANDINA N. On the choice of parameters in singular spectrum analysis and related subspace-based methods[J].Statistics and Its Interface,2010,3(3):259-279.
[18]	MAHMOUDVAND R, ZOKAEI M.On the singular values of the Hankel matrix with application in singular spectrum analysis[J].Chilean Journal of Statistics,2012,3(1):43-56.
[19]	VALLE Y, VENAYAGAMOORTHY G K,MOHAGHEGHI S,et al.Particle swarm optimization:Basic concepts,variants and applications in power systems[J].IEEE Transactions on Evolutionary Computation,2008,12(2):171-195.
[20]	KENNEDY J, EBERHART R C.Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks.Piscataway,NJ:IEEE Press,1995:1942-1948.
[21]	CHOWDHURY S, TONG W,MESSAC A,et al.A mixed-discrete particle swarm optimization algorithm with explicit diversity-preservation[J].Structural and Multidisciplinary Optimization,2013,47(3):367-388.
[22]	BOUBAKER S, DJEMAI M,MANANANNI N,et al.Active modes and switching instants identification for linear switched systems based on discrete particle swarm optimization[J].Applied Soft Computing,2014,14:482-488.
[23]	郭文忠,陈国龙. 离散粒子群优化算法及其应用[M].北京:清华大学出版社,2012:13-16. GUO W Z,CHEN G L.Discrete particle swarm optimization algorithm and its application[M].Beijing:Tsinghua University Press,2012:13-16(in Chinese).
[24]	KOROBEYNIKOV A. Computationk- and space-efficient implementation of SSA[J].Statistics and Its Interface,2010,3(3): 357-368.