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Forecast Verification Inputs — Record Quality Audit + Two-Measure Correlation
x402
Forecast Verification Inputs — Record Quality Audit + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Audits data quality and computes two-measure correlation for up to 200 forecast vs. observed value records, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Forecast verification inputs — Record quality audit + Two-measure correlation: Record quality audit + Two-measure correlation for supplied forecast verification inputs records (forecast_value, observed_value). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two reports: (1) a record quality audit identifying data issues such as missing values, malformed entries, or anomalies across the submitted rows, and (2) a two-measure correlation analysis quantifying the statistical relationship between forecast_value and observed_value columns — all computed locally without any external lookup or live data.
Example use cases
Weather station forecast accuracy check
“I have 120 rows of daily weather forecast and observed temperature data from two station groups across October 2026 — can you audit the record quality and compute the correlation between the forecast and observed values for me?”
Pre-analysis data validation for model output
“Before I run my analysis, can you check these 80 forecast-vs-observed records for any data quality problems and also tell me how correlated the forecast_value and observed_value columns are?”
Identifying bad rows in verification dataset
“I've got a set of forecast verification inputs with row IDs, dates, station groups, and both forecast and observed values — run a quality audit to flag any bad records and give me the two-measure correlation report on up to 200 rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-24-015?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and a correlation analysis on forecast vs. observed value pairs in a single call, without requiring live external data access or professional meteorological assessment. It is well-suited for pre-analysis validation workflows where you want to catch record issues and measure forecast-observation alignment on batches of up to 200 rows at low cost.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, forecast_value, observed_value) causes row-level rejection
- Malformed date strings not matching YYYY-MM-DD pattern cause validation failure
- All-null forecast_value or observed_value columns may prevent meaningful correlation computation
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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