Forecasting at Scale
Hierarchical reconciliation, probabilistic forecasts, global models and time-series foundation models.
12concepts
105flashcards
85minutes of reading
- 01 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.
- 02 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.
- 03 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.
- 04 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.
- 05 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.
- 06 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.