Symbolic Aggregate Features

Symbolic Aggregate Features is a dimensionality reduction technique in time series analysis that transforms continuous time series data into a discrete symbolic representation through piecewise aggregation and quantization, enabling efficient feature extraction and pattern discovery.

29

Publications

1.3K

Citations

96

Authors

46

Institutions

Publications per year

2017–2026

14

Authors

96

  1. CB
    Christian Braunagel

    Daimler (Germany), Germany

    1 publications · 43 citations

    1

    h-index

  2. CL
    Chengliang Liu

    Shanghai Jiao Tong University, China

    1 publications · 12 citations

    1

    h-index

  3. WC
    Wei Chen

    Zhejiang University, China

    1 publications · 34 citations

    1

    h-index

  4. TI
    Takayuki Itoh

    Ochanomizu University, Japan

    1 publications · 14 citations

    1

    h-index

  5. ZY
    Zhong Yuan

    Southwest Jiaotong University, China

    1 publications · 14 citations

    1

    h-index

1–5 of 96

Venues

  1. IEEE Access

    2 publications · 50 citations

    2

    h-index

  2. Measurement

    2 publications · 96 citations

    2

    h-index

  3. Multimedia Tools and Applications

    1 publications · 12 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 29 citations

    1

    h-index

  5. Nature

    1 publications · 23 citations

    1

    h-index

1–5