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Time Series Completeness — Record Quality Audit + String Normalisation + Base-100 Series
x402
Time Series Completeness — Record Quality Audit + String Normalisation + Base-100 Series 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).
Runs three local analytical reports on time series completeness data: a record quality audit, string normalisation, and a base-100 indexed series — for up to 200 rows of observed vs expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 analytical reports returned together: (1) a record quality audit flagging issues in the input rows, (2) a string normalisation report standardising text fields like dataset_group, and (3) a base-100 series re-indexing the chosen numeric field relative to a specified baseline row. All computed locally from the supplied data with no external lookups.
Example use cases
Pipeline completeness audit across groups
“I have daily completeness records for two dataset groups — group-a and group-b — with observed and expected point counts for each day in October 2026. Can you run all three reports (record quality audit, string normalisation, base-100 series indexed from row 0) on these 4 rows using observed_points as the field?”
Baseline-indexed completeness trend
“Take my 10-row time series completeness table with observed_points and expected_points for dataset group 'sensor-feeds' and generate a base-100 series indexed from row 2, along with a quality audit and string normalisation report.”
String normalisation for group labels
“My completeness records have inconsistent dataset_group labels like 'Group A', 'group-a', and 'GROUP A' — can you run the string normalisation and record quality audit reports on my 50 rows so I can see what needs cleaning? Use expected_points as the series field and index from row 0.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and base-100 indexing — on time series completeness data (observed vs expected points) in a single cost-efficient call. Ideal for data pipeline monitoring, completeness dashboards, or batch QA workflows where you want local computation with no external dependencies. Prefer it when you have up to 200 rows and want all three report types without making separate API calls.
Side effects
- Rows exceed 200-item limit — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not in YYYY-MM-DD format — schema validation failure
- base_index out of range (0–199) — error returned
- Null values in numeric fields may affect series computation
- field enum value not 'observed_points' or 'expected_points' — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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