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Time Series Completeness Audit — Record Quality, String Normalisation & IQR Outlier Flags
x402
Time Series Completeness Audit — Record Quality, String Normalisation & IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Audits time series completeness records (observed_points vs expected_points) for data quality issues, normalises strings, and flags IQR-based outliers across up to 200 rows in three independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags 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 covering: (1) a record quality audit identifying malformed or missing fields, (2) string normalisation results for text fields like dataset_group, and (3) IQR-based outlier flags for the selected numeric field (observed_points or expected_points). All reports are computed locally on the supplied rows with no external lookups.
Example use cases
Daily ETL pipeline completeness check
“I have 90 rows of daily ETL completeness data with observed_points and expected_points per dataset_group — can you audit them for record quality issues, normalise the group name strings, and flag any IQR outliers in observed_points so I can catch anomalies before my morning report?”
Sensor data coverage validation
“I'm tracking sensor data coverage across 150 dates and two sensor groups, with observed_points and expected_points for each day — please run all three quality reports on this so I can see which records have bad strings, quality issues, or outlier coverage readings.”
Data completeness monitoring for financial feeds
“We get daily financial feed completeness records and I want to validate 200 rows of observed vs expected data points — run the record quality audit, string normalisation, and IQR outlier detection on the expected_points field so I can flag anything suspicious before loading to our data warehouse.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, cost-effective ($0.01) trio of data quality checks — record auditing, string normalisation, and IQR outlier detection — applied to time series completeness data (observed vs expected point counts) in a single call. Ideal for pipelines that need lightweight pre-load validation without external dependencies or live lookups. Not suitable for prediction, forecasting, or datasets exceeding 200 rows.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, observed_points, expected_points) cause row-level audit failures
- Invalid date format (not YYYY-MM-DD) may cause parsing errors
- Null values in numeric fields reduce the effective sample for IQR calculation
- Payment of 0.01 USDC not completed — request not processed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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