Partial Autocorrelation Features

Partial Autocorrelation Features is a time series feature engineering method that quantifies the direct linear dependence between an observation and its lagged values at a specific lag, after removing the effect of intermediate lags, thereby isolating the unique correlation at each lag order.

10

Publications

529

Citations

31

Authors

18

Institutions

Publications per year

2017–2026

7

Authors

31

  1. PO
    Piotr Omenzetter

    University of Aberdeen, United Kingdom

    1 publications · 17 citations

    1

    h-index

  2. SM
    Shunyi Mao

    Shanghai Polytechnic University, China

    1 publications · 27 citations

    1

    h-index

  3. SO
    Seyed Omid Shahdi

    Qazvin Islamic Azad University, Iran

    1 publications · 14 citations

    1

    h-index

  4. MM
    Mahmood Mohassel Feghhi

    University of Tabriz, Iran

    1 publications · 14 citations

    1

    h-index

  5. ZB
    Zhiquan Bai

    Shandong University, China

    1 publications · 10 citations

    1

    h-index

1–5 of 31

Venues

  1. IET Image Processing

    1 publications · 14 citations

    1

    h-index

  2. IEEE Sensors Journal

    1 publications · 71 citations

    1

    h-index

  3. IEEE Access

    1 publications · 27 citations

    1

    h-index

  4. Energies

    1 publications · 293 citations

    1

    h-index

  5. Structural Health Monitoring

    SHM · 1 publications · 17 citations

    1

    h-index

1–5