Forecast Drift Detection

Forecast Drift Detection is a monitoring methodology in cloud forecasting that identifies systematic deviations between predicted and actual cloud resource demands over time, distinguishing itself by focusing on sustained performance degradation due to temporal distributional shifts in workload patterns.

8

Publications

264

Citations

33

Authors

21

Institutions

Publications per year

2017–2026

4

Authors

33

  1. KU
    Kamaluddeen Usman Danyaro

    Universiti Teknologi Petronas, Malaysia

    1 publications · 35 citations

    1

    h-index

  2. ES
    Everette S. Gardner

    University of Houston, United States

    1 publications · 70 citations

    1

    h-index

  3. AO
    Adalberto Ospino Castro

    University of the Coast, Colombia

    1 publications · 22 citations

    1

    h-index

  4. BA
    Bing Ai

    Huazhong University of Science and Technology, China

    1 publications · 20 citations

    1

    h-index

  5. AA
    Abdul Azeem

    Universiti Teknologi Petronas, Malaysia

    1 publications · 35 citations

    1

    h-index

1–5 of 33

Venues

  1. Journal of Forecasting

    1 publications · 70 citations

    1

    h-index

  2. Sustainability

    1 publications · 22 citations

    1

    h-index

  3. Monthly Weather Review

    1 publications · 30 citations

    1

    h-index

  4. Journal of Process Control

    1 publications · 20 citations

    1

    h-index

  5. Chaos Solitons & Fractals

    1 publications · 59 citations

    1

    h-index

1–5