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Time Series Completeness — Grouped Numeric Summary & Secondary-Measure Weighted Mean
x402
Time Series Completeness — Grouped Numeric Summary & 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 2026-10-28).
Computes a grouped numeric summary and a secondary-measure weighted mean for time series completeness records (observed vs. expected data points), processing up to 200 rows across two bundled analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Grouped numeric summary + Secondary-measure weighted mean: Grouped numeric summary + 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 bundled analytical reports: (1) a grouped numeric summary breaking down statistics (e.g. count, min, max, mean) for observed_points and expected_points segmented by dataset_group; and (2) a secondary-measure weighted mean computation using the selected primary field as weights. Results cover all submitted rows (up to 200) without any external lookups or live data enrichment.
Example use cases
Daily pipeline completeness audit
“I have a list of daily records for my data pipelines — each row has a group name, a date, how many data points we actually observed, and how many we expected. Can you group them and give me a numeric summary plus a weighted mean of observed_points across these groups?”
Multi-dataset coverage comparison
“We track data completeness across several dataset groups over the past month. Each record has an observed count and an expected count per date. Can you compute a grouped summary and a secondary-measure weighted mean so I can see which groups are underperforming?”
QA report on sensor data ingestion
“Our sensor ingestion pipeline logs expected vs. observed readings per sensor group each day. I have about 120 rows — can you produce a grouped numeric summary and weighted mean based on expected_points so I can share a completeness QA report with my team?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-096?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need grouped aggregate statistics and a weighted mean computed locally (no external APIs) on time series completeness data with observed vs. expected point counts, and you want two analytical reports bundled in a single low-cost call ($0.01 USDC for up to 200 rows). Prefer it over generic analytics tools when your data is already structured with group labels, dates, and completeness measures and you need a fast, serverless batch summary without setting up a data warehouse query.
Side effects
- Exceeding 200 rows causes rejection — keep input within maxItems limit
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) per row triggers validation error
- Invalid date format (must match YYYY-MM-DD pattern) causes schema validation failure
- Null values in observed_points or expected_points may affect weighted mean computation
- Submitting fewer than 1 row (empty array) violates minItems constraint
- Values outside the allowed numeric range (±1,000,000,000,000) will be rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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