An efficient improved singular spectrum analysis for processing GNSS position time series with missing data Article Swipe
Kunpu Ji
,
Yunzhong Shen
,
Fengwei Wang
,
Qiujie Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/gji/ggae381
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/gji/ggae381
SUMMARY The improved SSA (ISSA) method is widely recognized for directly extracting signals from gappy time-series without requiring prior interpolation. However, it is rather time consuming, particularly for long time-series with large window sizes, such as Global Navigation Satellite System (GNSS) position time-series. This study proposes an efficient ISSA method that yields equivalent results to the ISSA method while significantly reducing computation time. Both methods aim to minimize the quadratic norm of principal components, while our method has fewer unknown parameters in the principal component computation than those of the ISSA method. We evaluate the performance of the proposed method using real GNSS position time-series from 27 permanent stations located in mainland China. Results show that the proposed method can effectively reduce computation time than the ISSA method and the improvement depends on the chosen window size, the time-series length and the percentage of missing data. This efficient approach can be naturally extended to principal component analysis (PCA) and multichannel SSA (MSSA) for processing multiple incomplete time-series, improving computational efficiencies compared to the modified PCA and the improved MSSA while maintaining unchanged results. We also compare the ISSA method with the modified SSA (SSAM) and the iterative SSA methods using both real and synthetic time-series data. Results indicate that the ISSA method outperforms the SSAM method, and when conducted iteratively, also surpasses the iterative SSA method.
Related Topics
Concepts
Singular spectrum analysis
GNSS applications
Geodesy
Position (finance)
Series (stratigraphy)
Time series
Geology
Spectrum (functional analysis)
Remote sensing
Data processing
Algorithm
Computer science
Mathematics
Physics
Global Positioning System
Statistics
Telecommunications
Singular value decomposition
Database
Economics
Paleontology
Finance
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/gji/ggae381
- OA Status
- gold
- Cited By
- 4
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403754855
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403754855Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/gji/ggae381Digital Object Identifier
- Title
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An efficient improved singular spectrum analysis for processing GNSS position time series with missing dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-23Full publication date if available
- Authors
-
Kunpu Ji, Yunzhong Shen, Fengwei Wang, Qiujie ChenList of authors in order
- Landing page
-
https://doi.org/10.1093/gji/ggae381Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1093/gji/ggae381Direct OA link when available
- Concepts
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Singular spectrum analysis, GNSS applications, Geodesy, Position (finance), Series (stratigraphy), Time series, Geology, Spectrum (functional analysis), Remote sensing, Data processing, Algorithm, Computer science, Mathematics, Physics, Global Positioning System, Statistics, Telecommunications, Singular value decomposition, Database, Economics, Paleontology, Finance, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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50Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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