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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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