Feature Selection For Forecasting

Feature Selection for Forecasting is a methodological process in demand forecasting systems that identifies and retains the most relevant predictor variables from a larger set of potential inputs to improve forecast accuracy and model efficiency.

657

Publications

37.4K

Citations

2.1K

Authors

832

Institutions

Publications per year

2017–2026

481

Authors

2.1K

  1. FK
    Farshid Keynia

    Graduate University of Advanced Technology, Iran

    11 publications · 1K citations

    11

    h-index

  2. NA
    Nima Amjady

    Semnan University, Iran

    11 publications · 1.2K citations

    11

    h-index

  3. JW
    Jianzhou Wang

    Dongbei University of Finance and Economics, China

    8 publications · 611 citations

    8

    h-index

  4. NJ
    Nadeem Javaid

    COMSATS University Islamabad, Pakistan

    7 publications · 532 citations

    7

    h-index

  5. NG
    Noradin Ghadimi

    Islamic Azad University Ardabil, Iran

    5 publications · 823 citations

    5

    h-index

1–5 of 2.1K

Venues

  1. Expert Systems with Applications

    36 publications · 4.1K citations

    27

    h-index

  2. IEEE Access

    32 publications · 1.8K citations

    23

    h-index

  3. Energies

    31 publications · 2.4K citations

    22

    h-index

  4. Energy

    16 publications · 2.2K citations

    16

    h-index

  5. Applied Energy

    14 publications · 1.4K citations

    14

    h-index

1–5