Practical insights
- 1000published
- 25in review
- 8topic families
Results
- Drop or forecast any regressor that is not known at the forecast origin for the full horizon
A demand model uses yesterday's competitor fare as a feature, and the business needs forecasts 1 to 28 days ahead.
mediumdocumented fact - Report ex-ante accuracy, not ex-post, when a model uses future-unknown regressors
A team reports that adding temperature cut daily electricity-load forecast error by a third.
mediumdocumented fact - 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 - Put a gap between training and test windows when features or labels span time
Features include 14-day rolling sums, or labels mature only after a booking window closes.
mediumdocumented fact - Choose expanding or sliding training windows by backtest, not by habit
A logistics volume series changed behaviour after a network redesign two years ago, and the model trains on all history.
mediumpractitioner heuristic - 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 - Treat intermittent demand forecasts as rates and use bias-corrected Croston-type methods
A spare-parts series is zero most weeks with occasional orders of 3 to 20 units, and exponential smoothing chases each spike.
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 - Evaluate prediction intervals on coverage and width together
Two interval methods are compared, and the team picks the one with the highest coverage.
mediumdocumented fact - 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 - Score a quantile forecast with pinball loss at that quantile
A team forecasts the 0.9 quantile of demand and reports its MAPE against actuals.
mediumdocumented fact - Model each seasonal period explicitly in hourly or daily data
Hourly electricity load shows daily, weekly and annual cycles, and a model with one seasonal period leaves strong patterns in the residuals.
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 - Represent a known level shift with a step regressor or a post-break training window
A competitor added about 25% more seats on three routes from a known date, and the route forecasts now run high.
mediumdocumented fact - Bias-adjust back-transformed forecasts when totals or means matter
Daily demand is modelled on a log or Box-Cox scale, and the back-transformed daily forecasts are summed into a monthly budget.
mediumdocumented fact - Enforce positivity with a transformation, not by clipping negative forecasts to zero
A linear or ML forecast of low-volume sales produces negative values, and the pipeline replaces them with zero.
mediumdocumented fact - Difference only as much as a unit-root test and backtest support
A model is fitted on second differences because the first-differenced plot 'still looked wavy'.
mediumdocumented fact - Read ACF and PACF as hints, then choose orders by information criteria and residual checks
An analyst picks ARIMA(2,1,1) because the PACF 'cuts off at lag 2'.
mediumpractitioner heuristic - 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 - Detrend or difference the target before using tree models on trending series
A gradient-boosted forecast of a growing subscription base flattens at the highest level seen in training.
mediumdocumented fact - Test one global model across many related series before maintaining thousands of local ones
A team fits and monitors a separate ARIMA for each of 4,000 store-SKU series.
mediumempirical finding - Scale each series by its own recent level when training a global model
A global model across routes of 50 to 5,000 weekly passengers is accurate on big routes and poor on small ones.
mediumpractitioner heuristic - Choose recursive, direct or multi-output strategies by backtest at your horizons
A one-step-ahead ML model is fed its own predictions to produce a 28-day forecast.
mediumempirical finding - Backtest recursive forecasts with their own predicted lags, as production will run
A recursive LightGBM forecast reports similar error at day 1 and day 14.
mediumpractitioner heuristic - Treat public-benchmark scores of forecasting foundation models as possibly contaminated
A time-series foundation model is proposed because it tops a public leaderboard.
mediumdocumented fact - Make a foundation or deep forecaster beat seasonal naive and ETS on your backtest first
A team plans to replace its forecasting stack with a transformer-based model after a promising demo.
mediumempirical finding - Set low expectations and strict out-of-sample tests for financial return forecasts
A model with a strong in-sample fit is proposed to forecast monthly equity returns for allocation decisions.
mediumempirical finding - Use count-appropriate intervals for small integer series
Weekly claims at a small branch average three, and a normal-error model produces intervals from minus two to eight.
mediumpractitioner 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 - Freeze the event and schedule calendar as it stood at each origin before using it as a feature
A route-demand model uses an event table (strikes, airport works, schedule changes) that operations kept editing after the fact; the backtest reads the current table.
mediumpractitioner heuristic - Fit smoothers, interpolation and seasonal adjustment inside each backtest origin, because they look forward
A series is cleaned once with STL seasonal adjustment, centred moving averages and linear interpolation of gaps, and then split into train and test.
mediumdocumented fact - Split a panel of series by time for every series together, not by holding out whole series
A global model is trained on 200 routes; the backtest holds out 20 routes entirely and trains on the other 180 across all dates, including the dates being scored.
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 - Calibrate intervals separately at each horizon, since one-step residual spread is too narrow far ahead
A residual-based 80% interval built from one-step errors is reused for horizons 1 to 28.
mediumpractitioner heuristic - Check and fix quantile crossing before scoring or serving multi-quantile forecasts
A gradient-boosted model fits the 10th, 50th and 90th percentiles with separate runs and the dashboard sometimes shows the 90th below the median.
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 - Add non-negativity to reconciliation when the series are counts or sales
Route forecasts from a model are all non-negative, but after MinT reconciliation a thin route shows -3 passengers on some days.
mediumempirical finding - 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 - Monitor forecast error as a ratio to a frozen benchmark, not as an absolute number
A weekly dashboard alerts when MAPE exceeds 12%, and it fires every holiday season and stays quiet through real model decay in calm months.
mediumpractitioner heuristic - Choose between a deterministic and a stochastic trend on purpose, because the choice sets interval width
Annual passenger totals on a route have grown steadily for a decade, and two fitted models agree on the growth rate: a straight-line trend with ARMA errors, and an ARIMA model with one difference and drift.
mediumdocumented fact - Compare models by AICc only within one model class and one differencing order
A team puts ETS, ARIMA and an ARIMA with an extra difference into one AICc table and ships the lowest value.
mediumdocumented 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 - 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 - In a direct model, align future-known drivers with the target date and history features with the origin
A per-horizon boosted model for h = 1 to 14 days is built by shifting every column, including the promotion calendar, by h days.
mediumpractitioner heuristic - 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 - Route intermittent series to a method by interval and size variability, then confirm by backtest
A spare-parts catalogue of 20,000 items is forecast with a single method, although some items sell steadily and others sell every few months in varying quantities.
mediumempirical finding - Add a change-of-slope regressor when the growth rate shifts but the level does not jump
A competitor opens a route on a known date in 2024 and market volume does not drop at once; the growth rate slows from that date.
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 - Check that a zero-shot foundation forecaster can use your future-known drivers before expecting promotion peaks
A team evaluates an off-the-shelf pretrained forecaster on weekly sales with a known promotion calendar and finds that it misses every promotion peak.
mediumdocumented fact - Match a foundation model's context window to your longest seasonal cycle
Hourly electricity demand has weekly and annual cycles, and a pretrained forecaster gets the last 512 hours as context.
mediumempirical finding - Bag an ETS model with bootstrapped STL remainders when single fits are unstable
Monthly ETS forecasts for 400 series change noticeably each time the models are refitted, though the history changed by one point.
mediumempirical finding