Forecasting
Demand forecasting, backtesting, and prediction intervals that planners can use.
-
Forecast accuracy metrics and how to choose them
MAE, RMSE, MAPE, sMAPE, MASE, bias, interval coverage and pinball loss, what each one measures, where each misbehaves, and which to put in front of a planning audience.
-
Backtesting forecasts with rolling origins
Why one train/test split misleads, how rolling-origin evaluation works, expanding versus sliding windows, horizon-aware features, a compact Python loop, and the mistakes that make backtests flatter a model.
-
Hierarchical forecast reconciliation
Forecasting specialty demand that has to add up to the hospital total. Why incoherent forecasts start planning arguments, what bottom-up and top-down get wrong, how MinT reconciliation works, and a backtest in Python.
-
Prediction intervals for planners
Why a point forecast isn't a plan, how to build prediction intervals from backtest errors and quantile regression, how to check they hold, and how to choose and present a range for a staffing decision.