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

Authors

284

  1. HH
    Hossein Hassani

    University of Tehran, Iran

    4 publications · 290 citations

    4

    h-index

  2. SS
    Saeid Sanei

    University of Surrey, United Kingdom

    3 publications · 145 citations

    3

    h-index

  3. JZ
    Jaime Zabalza

    University of Strathclyde, United Kingdom

    2 publications · 107 citations

    2

    h-index

  4. MŠ
    Marinko Škare

    Juraj Dobrila University of Pula, Croatia

    2 publications · 35 citations

    2

    h-index

  5. BW
    Bingo Wing‐Kuen Ling

    Guangdong University of Technology, China

    2 publications · 36 citations

    2

    h-index

1–5 of 284

Venues

  1. 2

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  2. Journal of Coastal Research

    JCR · 2 publications · 40 citations

    2

    h-index

  3. 2

    h-index

  4. Biomedical Signal Processing and Control

    2 publications · 73 citations

    2

    h-index

  5. Measurement

    2 publications · 59 citations

    2

    h-index

1–5