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Proceedings Paper

Improved gap filling method based on singular spectrum analysis and its application in space environment
Author(s): Xiangzhen Li; Shuai Liu; Zhi Li; Jiancun Gong
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Paper Abstract

Data missing is a common phenomenon in the space environment measurements, which impacts or even blocks the following model-building procedures, predictions and posterior analysis. To fill these data gaps, an improved filling method based on iterative singular spectrum analysis is proposed. It first extracts a distribution array of the gaps and then fills the gaps with all known data. The distribution array is utilized to generate the test sets for cross validation. The embedding window length and principal components are determined by the discrete particle swarm optimization algorithm in a noncontinuous fashion. The effectiveness and adaptability of the filling method are proved by some tests done on solar wind data and geomagnetic indices from different solar activity years.

Paper Details

Date Published: 15 November 2017
PDF: 13 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060527 (15 November 2017); doi: 10.1117/12.2292785
Show Author Affiliations
Xiangzhen Li, Beijing Institute of Environmental Features (China)
Shuai Liu, People's Liberation Army (China)
Zhi Li, Equipment Academy (China)
Jiancun Gong, China Space Science Ctr. (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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