Time-series Feature Aggregation

Time-Series Feature Aggregation is a feature engineering method that systematically summarizes sequential observations from temporal data into fixed-length, interpretable features by applying statistical or domain-specific transformations over defined time intervals.

4

Publications

109

Citations

11

Authors

8

Institutions

Publications per year

2017–2026

2

Authors

11

  1. IM
    Inga M. Müller

    Technical University of Munich, Germany

    1 publications · 30 citations

    1

    h-index

  2. JD
    José del R. Millán

    École Polytechnique Fédérale de Lausanne, Switzerland

    1 publications · 31 citations

    1

    h-index

  3. XM
    Xiandong Ma

    Lancaster University, United Kingdom

    1 publications · 32 citations

    1

    h-index

  4. QM
    Qiucheng Miao

    National University of Defense Technology, China

    1 publications · 32 citations

    1

    h-index

  5. RC
    Ricardo Chavarriaga

    École Polytechnique Fédérale de Lausanne, Switzerland

    1 publications · 31 citations

    1

    h-index

1–5 of 11

Venues

  1. Energy and AI

    1 publications · 30 citations

    1

    h-index

  2. Computers & Electrical Engineering

    1 publications · 16 citations

    1

    h-index

  3. Renewable Energy

    1 publications · 32 citations

    1

    h-index

  4. IEEE Transactions on Biomedical Engineering

    1 publications · 31 citations

    1

    h-index

  5. The Journal of Open Source Software

    JOSS · 1 publications · 11 citations

1–5