Forecasting at Scale
Hierarchical reconciliation, probabilistic forecasts, global models and time-series foundation models.
5concepts
58flashcards
35minutes of reading
- 01 Global Models Versus Local Models Why fitting one model across thousands of series beats fitting one model per series, what cross-learning provides that per-series estimation cannot, and where local models still win.
- 02 Forecast Evaluation at Scale Why MAPE fails on the series that matter most, which scale-free metrics work instead, and how aggregating errors across a heterogeneous population hides the failures worth finding.
- 03 Hierarchical Forecasting and Reconciliation Why independently forecasting every level of a hierarchy produces numbers that do not add up, what reconciliation does about it, and why the optimal method also improves accuracy.
- 04 Probabilistic Forecasts and Quantile Loss Why a point forecast is insufficient for any decision involving asymmetric costs, how pinball loss trains quantiles directly, and what makes a distributional forecast well calibrated.
- 05 Time Series Foundation Models What a pretrained forecasting model transfers, why zero-shot forecasting is plausible at all, and how to evaluate the claim against a well-tuned classical baseline.