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Time Series Completeness — Numeric Distribution + Date by Category Pivot
x402
Time Series Completeness — Numeric Distribution + 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).
Generates two local analytical reports (numeric distribution and date-by-category pivot) for time series completeness data (observed_points, expected_points) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric distribution + Date by category pivot: Numeric distribution + Date by category pivot 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 analytical reports: (1) a numeric distribution of the selected field (observed_points or expected_points) across all supplied rows, and (2) a date-by-category pivot table breaking down the selected field by date and dataset_group. Both reports are computed locally with no external lookups, covering up to 200 input rows.
Example use cases
Pipeline completeness audit by group
“Here are 120 rows of daily completeness records for our data pipelines, each with a dataset_group, date, observed_points, and expected_points. Can you generate a numeric distribution and a date-by-category pivot on observed_points so I can see where coverage is dropping?”
Expected vs observed gap analysis
“I have time series completeness data for 4 groups over the last quarter — about 90 rows with date, dataset_group, observed_points, and expected_points. Run a numeric distribution and date-by-category pivot on the expected_points field so I can spot which groups consistently fall short.”
Multi-group data coverage summary
“Can you take these 200 completeness records covering dataset groups A, B, and C across 2025, and give me both a numeric distribution and a date-by-category pivot for observed_points? I want to see the spread and how coverage varies by group over time.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-083?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness records (observed vs expected points) and need two complementary analytical views — a numeric distribution and a date-by-category pivot — in a single low-cost call. Ideal for data engineers auditing pipeline completeness across multiple groups over time without needing live data fetching, prediction, or external enrichment.
Side effects
- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Null values for observed_points or expected_points may affect distribution accuracy
- Invalid field enum value (not 'observed_points' or 'expected_points') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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