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
Parents
8
Automatic monitoring of forecast errors
Everette S. Gardner · Journal of Forecasting · 1983 · 70 citations
Computing, Economics, Econometrics +16
Proactive QC: A Fully Flow-Dependent Quality Control Scheme Based on EFSO
Daisuke Hotta, Tse‐Chun Chen, Eugenia Kalnay et al. · Monthly Weather Review · 2017 · 30 citations
Deyslen Mariano-Hernández, Luís Hernández-Callejo, Martín Solís et al. · Sustainability · 2022 · 22 citations · Full text
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21
TU Dortmund University
Dortmund, Germany
1 h-index
2 publications
32 citations
Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology
Karachi, Pakistan
1 h-index
1 publications
35 citations
Dartmouth College
Hanover, United States
1 h-index
1 publications
70 citations
College of Business Administration
Riga, Latvia
1 h-index
1 publications
70 citations

Universiti Teknologi Petronas
Ipoh, Malaysia
1 h-index
4 publications
140 citations
University of Cuenca
Cuenca, Ecuador
1 h-index
1 publications
22 citations
Xi'an High Tech University
Xi'an, China
1 h-index
2 publications
118 citations

Instituto Tecnológico de Santo Domingo
Santo Domingo, Dominican Republic
1 h-index
2 publications
44 citations
1–8 of 21
1 publications · 70 citations
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1 publications · 22 citations
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1 publications · 30 citations
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1 publications · 20 citations
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1 publications · 59 citations
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