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 - Report error by horizon and evaluate at the horizon the decision uses
A dashboard shows one pooled error for a 1-to-90-day forecast that feeds pricing decisions made 21 days before departure.
easydocumented 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 - Compute WAPE at the level where actuals cannot sum to zero
WAPE is reported for every store-SKU-week cell and many cells are blank or infinite.
easypractitioner heuristic - Do not average MAPE across series of very different volumes
A portfolio report averages per-series MAPE, and a few slow movers make the whole forecast look broken.
easydocumented fact - Do not choose intermittent-demand models by MAE because it rewards forecasting zero
On a mostly-zero series, an all-zero forecast wins the MAE comparison against every model.
harddocumented 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 - Model one-off events as interventions and keep them out of learned seasonality
A city hosted a single major sports final, and hotel and flight demand spiked for one week.
easydocumented 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 - Shift before rolling so window features exclude the target period
A 7-day rolling mean of sales is computed and joined to the same row as the target.
easydocumented fact - Prefer simple models on very short series and say how little the backtest can tell you
A new route has 30 weeks of history, and someone proposes a seasonal ARIMA with many parameters.
easydocumented fact - Measure forecast value added at every step, including human overrides
Planners adjust the statistical forecast every month, and nobody knows whether the adjustments help.
easypractitioner heuristic - Track forecast bias separately from accuracy
A forecast with acceptable MAE consistently runs below actual demand on high-season weeks.
easypractitioner heuristic - Do not answer a price what-if by changing the price input of a demand forecast
A manager asks what bookings would be if fares were cut 5%, and the analyst re-scores the forecasting model with lower fares.
harddocumented fact - Model spill and unconstrained demand before estimating gains from added capacity
A planner asks for passenger gains if the aircraft on a route changes from 160 to 180 seats.
hardpractitioner heuristic - Evaluate an intervention against a counterfactual forecast with control series
A loyalty campaign launched on one market, and leadership wants to know whether bookings rose because of it.
harddocumented fact - Build an explicit time grid so missing periods are not mistaken for zeros or skipped
A route's daily table has a nine-day gap where no rows exist, and the lag-7 feature is computed with row offsets.
easypractitioner heuristic - 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 - End each backtest origin on a complete aggregation period
Daily bookings are rolled up to weeks, and the cutoff falls on a Wednesday; the training week's total contains three days and the test week begins mid-week.
easypractitioner 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 - Build the naive benchmark at the production horizon from the origin value, not from the previous day
A 28-day-ahead model is compared with a naive baseline computed as yesterday's actual for every target day.
easydocumented fact - 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 - Retire sMAPE from forecast reports because of its asymmetry and its failure at zero
A report uses symmetric MAPE because it is bounded and the team believes it fixes MAPE's bias toward low forecasts.
easydocumented fact - 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 - Put a sampling error on measured interval coverage before calling a model miscalibrated
An 80% interval covered 74% of 50 backtest origins and the team wants to retune the model.
easypractitioner heuristic - 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 - Replay intermittent-demand forecasts through a stock policy and report service and stock, not only error
Spare-parts demand is mostly zeros with occasional orders, and two methods tie on MASE.
hardpractitioner 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 - Report how many series the new model beats the benchmark on, next to the average improvement
A candidate model improves average MASE over 2,000 series by 4% and the team proposes a full cutover.
easypractitioner heuristic - Take the seasonal difference first and let a seasonal-strength measure decide whether you need one
Daily bookings show a strong weekly pattern and a trend, and an analyst applies a first difference and then wonders why the weekly spikes are still there.
easydocumented fact - Let a damped trend compete with a linear trend whenever automatic forecasts run beyond a few periods
A monthly forecast for 1,000 route-level series is produced automatically, and a linear-trend Holt model projects 24 months of continued growth on routes that recently grew.
easydocumented fact - 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 - Convert monthly totals to per-day or per-trading-day rates before modelling seasonality
Monthly revenue for a retailer swings partly because months contain different numbers of trading days, and the seasonal model learns a short-February dip and a long-month bump as seasonality.
easydocumented 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 - Check that the method tolerates gaps, and model systematic closures with dummies rather than interpolating them
A store's daily sales are missing on public holidays when it is closed, and the pipeline either fails inside an exponential smoothing fit or silently interpolates the closures.
easydocumented 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