Forecasting at Scale
Hierarchical reconciliation, probabilistic forecasts, global models and time-series foundation models.
12concepts
105flashcards
85minutes of reading
- 01 Direct versus Recursive Multi-Horizon Forecasting The two ways to turn a one-step model into a 28-step forecast, why theory prefers direct estimation and practice keeps preferring recursion, and the hybrid strategies that sit between them.
- 02 Exogenous Covariates and Known-Future Inputs The three-way split between static attributes, covariates known into the future and covariates observed only up to now, why conflating them creates a silent look-ahead bug, and what each one is worth.
- 03 Forecast Combination and the Diversity Benefit Why averaging forecasts has beaten selecting the best one for fifty years, the variance algebra that explains it, and the combination puzzle where estimated optimal weights lose to the simple mean.
- 04 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.
- 05 Gradient-Boosted Trees for Forecasting How reframing a forecast as tabular regression over lag and calendar features made LightGBM the default winner of large-scale forecasting competitions, and the three things that framing cannot do.
- 06 Intermittent Demand Forecasting Why series that are mostly zeros defeat ordinary smoothing and ordinary error metrics, how Croston, SBA and TSB separate demand size from demand occurrence, and what the M5 competition showed about where machine learning helps and where it does not.