Practical insights
- 1000published
- 25in review
- 8topic families
Results
- Check exchangeability before trusting split-conformal coverage on a time series
Split conformal intervals are calibrated on last year's residuals and promised to give 90% coverage.
harddocumented fact - Track realised interval coverage online and adjust the miscoverage level when it drifts
A deployed 90% interval for hourly call volumes covered 90% in backtests but 78% over the last month.
harddocumented fact - Decide explicitly how to treat a regime-change period in training and evaluation
Two years of history include an abrupt collapse and slow recovery in travel demand.
hardpractitioner heuristic - Log the action taken on each forecast so that monitored error is not contaminated by your own response
A capacity forecast triggers extra flights on routes forecast high, and the monitored error on those routes keeps improving.
hardpractitioner heuristic - Decide the ARIMA constant deliberately, because with differencing it sets the long-run trend
An automatic ARIMA run on quarterly fuel-cost data returns d = 1 with a non-zero constant, and the five-year forecast climbs in a straight line.
harddocumented fact - Train on the driver forecasts that existed at each origin, not on the values that were later observed
A day-ahead load model is trained on observed temperature, but in production it receives a weather forecast whose error grows with lead time.
hardpractitioner heuristic - Pick the MinT covariance estimator by the number of series relative to the history length
A retail group reconciles forecasts for 4,000 item-store series with 104 weeks of history, using MinT with the sample covariance of one-step forecast errors.
harddocumented fact - Weight a global model's loss by the weights of the metric that will judge it
A global boosted model for 30,000 SKU series is trained with unweighted squared error, but it will be judged on an error that is weighted by recent revenue.
hardpractitioner heuristic - Use a probability-updating method such as TSB when items can become obsolete
A wholesaler's slow items sometimes stop selling for good, yet a Croston forecast stays at the old demand rate for months because it only updates when a sale occurs.
harddocumented fact - Handle sparse and flat series before sending them to a mean-scaled, tokenizing forecaster
A pretrained model that scales each series by the mean absolute value of its context is run on spare-part demand where one week in twenty has a sale of one unit.
harddocumented fact - Feed a statistical model's out-of-sample forecast to the ML model as a feature, rebuilt at every origin
A boosted-tree forecaster is worse than ETS at trend and seasonal shape but better at promotions, and the team wants both strengths.
hardpractitioner heuristic