Practical insights
- 1000published
- 25in review
- 8topic families
Results
- Widen or label scenario forecasts because they exclude uncertainty in the assumed drivers
Finance asks for next quarter's demand under three fuel-price scenarios, each with an 80% interval.
mediumdocumented fact - Guard MASE against zero denominators and state which naive scaling you used
MASE is computed across thousands of SKUs and some values come back infinite or enormous.
mediumdocumented fact - Separate stockout zeros from true zero demand before modelling a retail series
A store-SKU series looks intermittent, and stock records show the item was frequently unavailable.
mediumpractitioner heuristic - Reconcile hierarchical forecasts so route, region and network numbers agree
Route-level and network-level demand forecasts are produced separately, and the routes do not add up to the network total.
mediumdocumented fact - Match the forecasting level of a hierarchy to the noise and the decision
Store-SKU daily sales are too noisy to forecast well, but replenishment decisions are made per store and SKU.
mediumpractitioner heuristic - Check interval coverage by season and segment, not only overall
Overall coverage of a 90% demand interval is 89%, and the team declares it calibrated.
mediumpractitioner heuristic - Pick the forecast quantile from the ratio of under- and over-forecast costs
Operations asks for 'the forecast' to set meal loads per flight, where running out costs more than waste.
mediumdocumented fact - Handle weekly data's non-integer annual season with Fourier terms or STL
A weekly demand series is fitted with a 52-period seasonal model, and the holiday peaks drift by a week every few years.
mediumdocumented fact - Encode moving holidays as windows before and after, not as a single day flag
A Turkish domestic route forecast misses the travel surge around Eid holidays, which move about eleven days earlier each Gregorian year.
mediumdocumented fact - Use bookings already on hand as a known-at-origin input for departure-level forecasts
A revenue-management team forecasts final bookings for a departure 21 days out using only past departures.
mediumempirical finding - Store first-reported and final actuals so forecast accuracy uses the right version
Revenue figures are updated days after the flight date, and accuracy reports are rerun against the latest table.
mediumpractitioner heuristic - Score lead-time demand as a cumulative quantity when the decision covers the whole lead time
A replenishment or seat-allocation decision commits stock for the next four days; the team reports daily MAPE for horizons 1 to 4.
mediumpractitioner heuristic - Align the seasonal benchmark to moving holidays, not to the same weekday last year
A seasonal naive forecast (value 364 days ago) is the benchmark for daily demand around religious holidays whose date moves about 10 to 11 days earlier each Gregorian year.
mediumpractitioner heuristic - Aggregate ratios of errors to a benchmark with a geometric mean, not an arithmetic mean
Model A is compared with seasonal naive on 500 SKUs by averaging each SKU's error ratio (model error divided by benchmark error).
mediumempirical finding - Say whether a point forecast targets the mean or the median, and score it consistently
A skewed demand model is trained on squared error but selected on MAE, and two teams argue over which candidate is more accurate.
mediumdocumented fact - Split squared forecast error into bias and variance across origins before choosing a fix
A model's RMSE is above target and the team is deciding between a bias correction, more regularisation and an ensemble.
mediumpractitioner heuristic - Never add per-item quantile forecasts to get a quantile for the total
A planner sums the 90th-percentile forecasts of 40 flights to size a network-wide buffer.
mediumdocumented fact - Divide CRPS or interval scores by a benchmark's score before averaging across series
A portfolio of 300 routes reports mean CRPS per model, and the average is driven by five large trunk routes.
mediumpractitioner heuristic - Evaluate reconciled forecasts level by level against the base forecasts, since coherence is not accuracy
A reconciled forecast sums exactly and the team ships it on the strength of a better headline number at the network level.
mediumempirical finding - Difference the response and every predictor together in a dynamic regression
Weekly ticket sales are regressed on a price index, search volume and a trend; the response needs one difference to be stationary, but the predictors are left in levels.
mediumdocumented fact - Look for outliers in the robust STL remainder, because thresholds on the raw series flag seasonal peaks
A z-score rule on daily demand flags every December peak and misses a one-day system-outage dip in March.
mediumdocumented fact - Treat cross-classified series as grouped, not as an arbitrary single tree
Seats sold are available by route, cabin and channel, and an analyst forces them into route, then cabin, then channel to use a hierarchical method, although channel totals across routes are also reported.
mediumdocumented fact - Include lagged promotion or advertising effects and pick the lag count by AICc on a common sample
Weekly sales respond to a campaign over several weeks, but the model contains only this week's campaign flag.
mediumdocumented fact