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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).
Monitoring Machine Learning Forecasts for Platform Data Streams
To ensure accurate and stable forecasts, we propose a simple data-driven monitoring procedure to answer the question when the ML algorithm should be re-trained.
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities.
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