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Forecast Verification Inputs — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean
x402
Forecast Verification Inputs — Record Quality Audit + String Normalisation + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Runs three local analytical reports (record quality audit, string normalisation, and secondary-measure weighted mean) on up to 200 forecast verification rows containing forecast and observed values.
Price / call$0.01
Success—
StatusUnknown
How this service works
Forecast verification inputs — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied forecast verification inputs records (forecast_value, observed_value). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 ro
Output
Three separate analytical reports: (1) a record quality audit flagging issues in the input rows, (2) a string normalisation report cleaning up station_group and other string fields, and (3) a secondary-measure weighted mean computed over the specified numeric field (forecast_value or observed_value) across all submitted rows. All processing is performed locally with no external lookups.
Example use cases
Pre-analysis quality check on forecast data
“I have a batch of 50 weather station forecast vs observed readings for October. Can you audit them for record quality issues, normalise the station group strings, and give me a weighted mean of the forecast_value field? Here are the rows with row IDs, station groups, dates, forecast values, and observed values.”
Weighted mean summary for observed values
“Run the three analytical reports — record quality audit, string normalisation, and weighted mean — on these 30 forecast verification rows using observed_value as the field for the weighted mean calculation.”
Data hygiene check before model evaluation
“Before I hand off these forecast verification records to our model evaluation pipeline, can you check them for quality issues, normalise any inconsistent station group strings, and compute the secondary-measure weighted mean? I'll supply up to 200 rows with their row IDs, dates, station groups, forecasts, and observed values.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-24-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous local analytical operations — quality audit, string normalisation, and weighted mean — on forecast/observed value pairs in a single $0.01 call. It is purpose-built for forecast verification data with station_group and date dimensions. Prefer it over single-report endpoints when you need all three reports together for up to 200 rows without any live external data fetching or prediction.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, station_group, date, forecast_value, observed_value) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Field selector not one of 'forecast_value' or 'observed_value' — enum validation error
- All numeric values null — weighted mean cannot be computed meaningfully
- Payment not completed — endpoint returns 402 or similar payment-required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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