Temporal Missingness Features
Temporal Missingness Features is a temporal feature engineering construct that encodes patterns of missing data over time by capturing the temporal structure and duration of gaps in time series observations, distinguishing it from general imputation or static missingness indicators through its explicit modeling of time-based absence dynamics.
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Publications
445
Citations
20
Authors
16
Institutions
Publications per year
2017–2026
2
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6
Paul Alexander Bloom, Ranqing Lan, Hanga Galfalvy et al. · Journal of Psychopathology and Clinical Science · 2024 · 20 citations · Full text
Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data
Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo et al. · 2017 · 19 citations
Computing, Data Science, Economics +17
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University of Toronto
Toronto, Canada
1 h-index
2 publications
26 citations
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Wuhan, China
1 h-index
2 publications
640 citations
EURECOM
Valbonne, France
1 h-index
2 publications
68 citations
Laboratoire d’Océanographie de Villefranche
Villefranche-sur-Mer, France
1 h-index
1 publications
41 citations
National Research Council
Rome, Italy
1 h-index
1 publications
41 citations
Stony Brook University
Stony Brook, United States
1 h-index
1 publications
41 citations
State University of New York
Albany, United States
1 h-index
1 publications
41 citations
Virginia Tech
Blacksburg, United States
1 h-index
3 publications
57 citations
1–8 of 16
1 publications · 320 citations
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1 publications · 13 citations
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1 publications · 41 citations
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1 publications · 10 citations
1 publications · 13 citations
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