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Time Series Completeness — Record Quality Audit + Two-Measure Correlation
x402
Time Series Completeness — 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 2026-10-28).
Audits record quality and computes two-measure correlation for time series completeness data (observed vs expected data points), processing up to 200 rows and returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Two-measure correlation: Record quality audit + Two-measure correlation for supplied time series completeness records (observed_points, expected_points). 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 independent analytical reports: (1) a record quality audit covering the completeness records (flagging anomalies, nulls, or out-of-range values in observed_points and expected_points), and (2) a two-measure correlation analysis between observed_points and expected_points across the supplied time series rows. No external lookups or predictions are performed; all analysis is based solely on the supplied data.
Example use cases
Daily pipeline completeness audit
“I have 90 days of pipeline monitoring data with observed and expected data point counts per day across two dataset groups — can you audit the record quality and compute the correlation between observed and expected points so I can see where our completeness drops off?”
Multi-group sensor data gap detection
“Our sensor network logs expected vs observed readings daily across several sensor groups from 2025-01-01 to 2025-03-31 — run a quality audit and correlation analysis on that completeness data so I can identify which groups have the worst gaps.”
ETL job completeness regression check
“After our ETL refactor, I want to check if observed data point counts still track expected counts correctly — can you audit the record quality and correlate the two measures across our 150-row daily completeness log?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-015?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a data quality audit and a correlation analysis on time series completeness records in a single $0.01 call, without requiring any live data lookup, external enrichment, or predictive modeling. Ideal for batch validation of up to 200 rows of observed vs expected data point counts grouped by dataset and date.
Side effects
- Exceeding 200 row limit causes rejection
- Missing required fields (row_id, date, observed_points, expected_points) causes validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Payment failure or insufficient USDC balance blocks processing
- Null values in observed_points or expected_points may reduce correlation reliability
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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