Regression-based Temperature Forecasting

Regression-Based Temperature Forecasting is a temperature forecasting method that employs regression analysis to model the relationship between meteorological variables and temperature outcomes, distinguishing itself by using statistical or machine learning regression techniques to predict continuous temperature values from historical and environmental data.

67

Publications

3.1K

Citations

246

Authors

125

Institutions

Publications per year

2017–2026

41

Authors

246

  1. SS
    Sancho Salcedo‐Sanz

    Universidad de Alcalá, Spain

    3 publications · 269 citations

    3

    h-index

  2. FA
    Feras Al‐Obeidat

    Zayed University, United Arab Emirates

    2 publications · 67 citations

    2

    h-index

  3. YZ
    Yuan Zhang

    Chinese Research Academy of Environmental Sciences, China

    2 publications · 45 citations

    2

    h-index

  4. BS
    Bruce Spencer

    University of New Brunswick, Canada

    2 publications · 67 citations

    2

    h-index

  5. SD
    Shanshan Duan

    China University of Geosciences, China

    2 publications · 45 citations

    2

    h-index

1–5 of 246

Venues

  1. Neural Computing and Applications

    3 publications · 299 citations

    3

    h-index

  2. Applied Sciences

    2 publications · 63 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 264 citations

    2

    h-index

  4. Theoretical and Applied Climatology

    2 publications · 141 citations

    2

    h-index

  5. Energy

    2 publications · 184 citations

    2

    h-index

1–5