Model training
The metrics on this page measure the quality of the base statistical model that generates the AI forecast. They do not reflect manual adjustments made by Sales, Marketing, or Consensus; to evaluate those adjustments, see Forecast accuracy and Forecast Value Added (FVA).
These metrics are computed internally during the model training and algorithm selection process. Their primary audience is technical (data science / analytics team). Business users can safely disregard them.
Metrics catalogue
| KPI | Full name | What it measures | Unit | Good when |
|---|---|---|---|---|
| MASE | Mean Absolute Scaled Error | Absolute error normalised against a naive reference model (random walk) | Dimensionless | < 1 (better than naive) |
| WAPE | Weighted Absolute Percentage Error | Demand-weighted MAPE; robust to low-volume SKUs | % | Minimum |
| wQL | Weighted Quantile Loss | How well the model estimates demand distribution quantiles (confidence intervals) | Dimensionless / normalised | Minimum |
| MSE | Mean Squared Error | Same as ECM in the accuracy section; penalises large errors | Units² | Minimum |
The MSE (Mean Squared Error) in this training section is the same indicator as the ECM (Error Cuadrático Medio) documented in Forecast accuracy. The formula is identical: (1/n) · Σ(Dᵢ − Fᵢ)². The difference is purely one of naming convention.
Description of each metric
MASE
MASE scales the model's MAE against the MAE of a simple reference model (typically a random walk or seasonal lag-1 difference). MASE < 1 means the model outperforms the naive benchmark; MASE > 1 means it performs worse.
TODO: exact formula (pending confirmation from the analytics team).
WAPE
WAPE weights the percentage error by each SKU's demand, so high-volume SKUs carry more influence. This makes it more stable than MAPE when the portfolio includes very low-demand or intermittent items.
TODO: exact formula (pending confirmation from the analytics team).
wQL (weighted Quantile Loss)
wQL evaluates the quality of the model's probabilistic estimates: how well calibrated the quantiles are (for example, the 0.9 quantile should cover 90 % of real observations). It is especially relevant for safety stock calculations and fill rate targets.
TODO: exact formula (pending confirmation from the analytics team).
MSE
See ECM in Forecast accuracy. Formula: (1/n) · Σ(Dᵢ − Fᵢ)².
Relationship to model selection
AInventory evaluates several time-series algorithms and selects the model with the best aggregate performance on these metrics over the validation set (held-out historical data). The selected model produces the Baseline (AI) visible in the forecast module.