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Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e. g., geographical regions) and temporal aggregation (e. g., minutes to days).
Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions
Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables.
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies.
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