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Time Series Completeness: Category Frequencies + Secondary-Measure Weighted Mean
x402
Time Series Completeness: Category Frequencies + Secondary-Measure Weighted Mean 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).
Computes category-level frequencies and a secondary-measure weighted mean for up to 200 time series completeness records (observed vs. expected points), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Category frequencies + Secondary-measure weighted mean: Category frequencies + Secondary-measure weighted mean 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 independent reports: (1) category frequencies showing how often each dataset_group appears across the submitted rows, and (2) a weighted mean of the chosen primary field (observed_points or expected_points) using the secondary measure as weights — all computed locally from the supplied data, with no external lookups or predictions.
Example use cases
Data pipeline completeness audit
“I have 50 daily rows from our ETL pipeline tracking observed vs expected data points for three dataset groups from January 2026 — can you compute the category frequencies and weighted mean of observed_points across those groups so I can see which ones are consistently under-delivering?”
Weekly sensor coverage summary
“Here are 120 completeness records for our IoT sensor network grouped by region, each with a date, observed_points, and expected_points — run the category frequency and secondary-measure weighted mean report on expected_points so we can assess coverage balance across regions.”
QA report for dataset groups
“I've got completeness records for two dataset groups over the past month — about 80 rows total — with observed and expected point counts per day. Can you give me category frequencies and a weighted mean on observed_points to include in our weekly QA summary?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-109?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 pre-collected time series completeness records (observed vs. expected point counts) and need both a categorical frequency breakdown by group and a weighted mean of one numeric measure — all in a single low-cost call. Prefer it over general-purpose analytics APIs when your data is already structured with dataset_group, date, observed_points, and expected_points fields and you want two reports for $0.01 USDC without sending data to a heavyweight analytics platform.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD format — schema validation failure
- Null values in numeric fields may affect weighted mean calculation
- Invalid field enum value (not 'observed_points' or 'expected_points') — rejected
- Empty rows array — minItems violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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