High-dimensional Forecasting
High-Dimensional Forecasting is a forecasting methodology characterized by the prediction of multivariate time series in which the number of variables approaches or exceeds the number of observational time points, necessitating specialized statistical or computational techniques to address issues of dimensionality, collinearity, and overfitting.
99
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5.9K
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306
Authors
184
Institutions
Publications per year
2017–2026
65
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99
Temporal pattern attention for multivariate time series forecasting
Shun-Yao Shih, Fan-Keng Sun, Hung-yi Lee · Machine Learning · 2019 · 931 citations · Full text
Computing, Data Science, Forecasting +11
Scaled PCA: A New Approach to Dimension Reduction
Dashan Huang, Fuwei Jiang, Kunpeng Li et al. · Management Science · 2021 · 243 citations
Computing, Data Science, Forecasting +11
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184
The University of Tokyo
Tokyo, Japan
4 h-index
6 publications
325 citations

Sun Yat-sen University
Guangzhou, China
3 h-index
7 publications
281 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
4 publications
216 citations
Pontifícia Universidade Católica do Rio de Janeiro
Rio de Janeiro, Brazil
3 h-index
11 publications
439 citations

Soochow University
Suzhou, China
3 h-index
3 publications
174 citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
13 publications
184 citations

China University of Geosciences (Beijing)
Beijing, China
2 h-index
4 publications
148 citations

University of Salerno
Fisciano, Italy
2 h-index
3 publications
71 citations
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7 publications · 523 citations
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7 publications · 589 citations
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Repository · 5 publications · 400 citations
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3 publications · 188 citations
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3 publications · 424 citations
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