Description
Qualify an explicit scaled-error denominator by seasonal-period wording and source.
Prediction metrics depend on the observation definition, reference forecast, denominator sign and aggregation. A single error contribution differs from a mean score or normalized skill measure. Interval midpoints, squared probability errors and seasonal-denominator labels must retain the exact convention before models are compared.
Qualify an explicit scaled-error denominator by seasonal-period wording and source.
When to use this tool
Use Scaled Error Seasonal Denominator Tag when you already have metricId, seasonalPeriodText, denominatorSource and need to qualify an explicit scaled-error denominator by seasonal-period wording and source.
How to use the tool and how it works
- Enter metricId, seasonalPeriodText, denominatorSource. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read token. Use the output convention below.
Enter metricId, seasonalPeriodText, denominatorSource using the field constraints below. Encode these values, in exactly that order, as a JSON array and return the serialized array as text in token. String values are preserved without trimming or external terminology lookup.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| metricId | Metric Id | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| seasonalPeriodText | Seasonal Period Text | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| denominatorSource | Denominator Source | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| token | Token | Required. Text; maximum UTF-16 length 8192. |
{
"input": {
"metricId": "MET17",
"seasonalPeriodText": "12",
"denominatorSource": "Training set A"
},
"output": {
"token": "[\"MET17\",\"12\",\"Training set A\"]"
}
}Limitations and assumptions
- The result records supplied values without calculating or inferring the denominator. Sources and asserted relationships are not independently authenticated.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Alternative or Complementary methods
Calculate the complete documented metric over the intended observations and reference baseline. Review missing/zero actuals, aggregation weights and outcome coding; one contribution or source label does not establish overall forecasting quality.
References
Similar or alternative tools
- Mape Absolute Vs Signed Actual Policy
Calculate one absolute prediction error under a chosen actual-value denominator convention.
- Interval Midpoint Bias Contribution
Calculate a prediction interval's midpoint and signed deviation from an actual value.