15 November 2017 Improved gap filling method based on singular spectrum analysis and its application in space environment
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060527 (2017) https://doi.org/10.1117/12.2292785
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
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.
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Xiangzhen Li, Xiangzhen Li, Shuai Liu, Shuai Liu, Zhi Li, Zhi Li, Jiancun Gong, Jiancun Gong, } "Improved gap filling method based on singular spectrum analysis and its application in space environment", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060527 (15 November 2017); doi: 10.1117/12.2292785; https://doi.org/10.1117/12.2292785
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