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.
- 07 Cold-Start Forecasting for New Series What to predict for a series with no history, why attribute-based pooling and pretrained priors are two answers to the same question, and how to evaluate a regime you cannot backtest in the usual way.
- 08 Deep Forecasting Architectures: DeepAR, N-BEATS and TFT Three neural forecasting designs that each made a different bet (autoregressive likelihoods, deep residual stacks, and attention over typed covariates), what each is good for, and the evidence that simple baselines still beat many deep models.
- 09 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.
- 10 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.
- 11 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.
- 12 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.