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Time Series Completeness Audit: Record Quality, String Normalisation & Two-Measure Correlation
x402
Time Series Completeness Audit: Record Quality, String Normalisation & 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).
Runs three bundled analytical reports on time series completeness data (observed_points vs expected_points): a record quality audit, string normalisation, and two-measure correlation — all offline, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied time series completeness records (observed_points, expected_points). 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 rows.
Output
Three independent analytical reports: (1) a record quality audit flagging anomalies or issues in the supplied rows, (2) a string normalisation report for text fields like dataset_group, and (3) a two-measure correlation report quantifying the relationship between observed_points and expected_points across the time series. All computed locally on the supplied data — no external lookups or predictions.
Example use cases
Daily pipeline completeness audit
“I have 30 days of pipeline completeness records with observed and expected data point counts per day — can you run a record quality audit, normalise the dataset group strings, and show me the correlation between observed and expected points across those rows?”
Multi-group sensor data completeness check
“We track sensor readings across two groups daily and want to know if the data is complete. Here are 60 rows with observed_points and expected_points per date — please audit the record quality and correlate the two measures for us.”
Data warehouse ingestion validation
“Before we sign off on last month's data warehouse ingestion, can you audit the completeness records for up to 150 rows, clean up the dataset_group strings, and tell us how correlated our observed versus expected ingestion counts are?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, correlation) on time series completeness data in a single low-cost call. Ideal for batch offline analysis of up to 200 rows of observed vs expected data point records where no live data lookup, prediction, or external action is required. Best suited when cost efficiency matters and you want all three report types together rather than calling separate specialised services.
Side effects
- Exceeding 200 rows returns a validation error
- Rows missing required fields (row_id, date, observed_points, expected_points) are rejected
- Dates not matching YYYY-MM-DD pattern cause schema validation failure
- Null values in both numeric measures may degrade correlation output
- Empty rows array is rejected per minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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