Singular Spectrum Analysis Features
Singular Spectrum Analysis Features is a time series feature extraction method that decomposes a time series into interpretable components through singular value decomposition of a trajectory matrix, enabling the identification of underlying patterns such as trends, oscillations, and noise.
83
Publications
3.7K
Citations
284
Authors
145
Institutions
Publications per year
2017–2026
43
83
1–5 of 83
284
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145
Cardiff University
Cardiff, United Kingdom
4 h-index
10 publications
732 citations

Central South University
Changsha, China
3 h-index
8 publications
715 citations

Xi'an Jiaotong University
Xi'an, China
2 h-index
5 publications
139 citations

University of Surrey
Guildford, United Kingdom
2 h-index
6 publications
460 citations
Université Paris Cité
Paris, France
2 h-index
4 publications
62 citations

University of Strathclyde
Glasgow, United Kingdom
2 h-index
5 publications
239 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
2 publications
103 citations
King Mongkut's Institute of Technology Ladkrabang
Bangkok, Thailand
2 h-index
2 publications
61 citations
1–8 of 145
2 publications · 33 citations
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JCR · 2 publications · 40 citations
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2 publications · 89 citations
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2 publications · 73 citations
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2 publications · 59 citations
2
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1–5