Reading your forecast
How to interpret point forecasts, selected models, scores, and cold-start results.
The forecast chart
The chart places historical demand and the future point forecast on the same monthly timeline. Each forecast value is y_hat for one DFU and one future ds month.
This delivery produces point forecasts only. It does not calculate or display confidence intervals. Treat the forecast as a model estimate, not as a guaranteed upper or lower bound.
Selected model and score
Each DFU result contains:
| Field | Meaning |
|---|---|
| best_model | The model selected for that DFU from the configured candidates |
| metric | The metric configured for model selection |
| score | The selected model's value for that metric; it may be unavailable for a cold-start DFU |
| cold_start | Whether the DFU was forecast without normal historical model selection |
| cold_start_method | The fallback method used when cold_start is true |
The available configured metrics are:
| Metric | What it measures |
|---|---|
| WMAPE | Absolute error weighted by actual demand |
| MASE | Absolute error scaled against a naive in-sample error |
| MAE | Average absolute error in demand units |
| RMSE | Error in demand units with more weight on larger misses |
| MAPE | Average percentage error; interpret carefully near zero demand |
Lower scores generally indicate less error when DFUs use the same metric. Do not compare raw MAE or RMSE values across DFUs with very different volumes without considering scale.
The result does not include bias, a naive-baseline comparison, confidence intervals, or scores for every candidate model. best_model is the selected result, not a complete model leaderboard.
Cold starts
A cold start is a DFU that cannot use the normal historical evaluation. The engine reports cold_start: true and one of these methods:
client_relation— use the relation configured by the planner;similarity_analog— borrow a pattern from a similar DFU;hierarchy_share— derive demand from the hierarchy;mean— use a mean-based fallback.
When the method is client_relation, confirm that the source DFU is still a meaningful analogue. A cold-start score can be null because there is not enough actual history for the selected metric.
Reviewing the portfolio
Start with DFUs that combine material forecast volume with a high score. An aggregate portfolio value can hide a small number of operationally important DFUs.
Then check:
- whether the historical series matches the cleaned monthly source data;
- whether the selected metric is appropriate for the DFU's scale and zero frequency;
- whether the DFU is a cold start;
- whether known-future regressors were populated in the uploaded future rows.
When the forecast disagrees with the commercial view
Check in this order:
- History — does monthly
yreflect the business event correctly? - Dimensions — does the selected hierarchy define the intended DFU?
- Regressors — was a planned event present in the input, including future months when known?
- Cold start — did the engine use a fallback or client relation?
- Configuration — were the intended models, horizon, metric, and reconciliation selected?
If the input itself is wrong, correct it in a new planning scenario. If only the run parameters need to change, use the adjust and reprocess flow.
Exporting
The forecast CSV contains the run, DFU identifier and dimensions, monthly ds, point forecast y_hat, best_model, metric, score, cold-start flag, and cold-start method. The exceptions export identifies cold starts, missing scores, and high-error results.
Exports do not contain confidence intervals, bias, baseline comparisons, or per-model comparison results.