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Time Series Completeness — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Time Series Completeness — Record Quality Audit + String Normalisation + Date by Category Pivot 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 (observed vs expected points): record quality audit, string normalisation, and date-by-category pivot — for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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
Returns three independent analytical reports computed locally against the supplied rows: (1) a record quality audit flagging malformed, null, or out-of-range values; (2) a string normalisation report cleaning and standardising dataset_group and other string fields; and (3) a date-by-category pivot table aggregating the selected numeric field (observed_points or expected_points) across dates and dataset groups. All reports are based solely on the submitted data with no external lookups.
Example use cases
Data pipeline completeness audit
“I have 80 rows of time series completeness data with observed and expected point counts per day for two dataset groups. Can you run a record quality audit, normalise the group name strings, and pivot the results by date and category so I can see where my pipeline is dropping data?”
IoT sensor coverage check
“Here are 150 rows tracking daily observed vs expected readings from our IoT sensors, grouped by sensor type and date. Run the three analytical reports — quality audit, string normalisation, and date-by-category pivot — focusing on the observed_points field so we can spot coverage gaps.”
Monthly reporting dataset validation
“Before I hand off this completeness report to the business team, can you quality-audit these 200 rows, normalise the category label strings, and give me a date-by-category pivot on the expected_points? The data covers multiple dataset groups across the last quarter.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and date-by-category pivot — on time series completeness data (observed vs expected points) in a single low-cost call. It is best suited for offline/batch data validation workflows where no live data lookup or prediction is needed and the dataset fits within 200 rows. Prefer alternatives if you need real-time streaming analysis, ML-based forecasting, or a single report type without the bundled package.
Side effects
- Exceeding 200 rows causes rejection — trim input to at most 200 items
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) causes validation failure
- Dates not matching YYYY-MM-DD pattern are rejected
- Null numeric values are allowed but may affect pivot and audit output quality
- Invalid field enum value (anything other than observed_points or expected_points) will error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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