Temporal Cross-validation
Temporal Cross-Validation is a time-series validation technique that belongs to the class of cross-validation methods, distinguished by its sequential splitting of data into temporally ordered training and testing sets to prevent leakage of future information and to evaluate model performance under realistic temporal dependencies.
18
Publications
900
Citations
105
Authors
58
Institutions
Publications per year
2017–2026
11
Parents
18
Ellen M. Considine, Colleen E. Reid, Michael Ogletree et al. · Environmental Pollution · 2020 · 90 citations · Full text
Masahito Katsuki, Muneto Tatsumoto, Kazuhito Kimoto et al. · Headache The Journal of Head and Face Pain · 2023 · 55 citations · Full text
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Leiden University Medical Center
Leiden, The Netherlands
2 h-index
5 publications
178 citations
Bell Biosystems (United States)
Berkeley, United States
1 h-index
4 publications
220 citations
Nanao hospital
Nanao, Japan
1 h-index
1 publications
55 citations
United States Agency for International Development
Washington D.C., United States
1 h-index
2 publications
58 citations

University of Amsterdam
Amsterdam, The Netherlands
1 h-index
1 publications
39 citations
University of Lausanne
Lausanne, Switzerland
1 h-index
1 publications
192 citations

Universidad de Santiago de Chile
Santiago, Chile
1 h-index
1 publications
23 citations
Monash Health
Melbourne, Australia
1 h-index
4 publications
72 citations
1–8 of 58
1 publications · 18 citations
1
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1 publications · 23 citations
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1 publications · 90 citations
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1 publications · 24 citations
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1 publications · 192 citations
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