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Time Series Completeness — Numeric Distribution + Secondary-Measure Weighted Mean
x402
Time Series Completeness — Numeric Distribution + 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 numeric distribution statistics and a secondary-measure weighted mean for time series completeness records (observed_points vs expected_points), producing two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric distribution + Secondary-measure weighted mean: Numeric distribution + 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 analytical reports based on the supplied rows: (1) a numeric distribution summary for the selected field (observed_points or expected_points) across the dataset, and (2) a secondary-measure weighted mean. Both reports are computed locally against the provided data with no external lookups. Up to 200 rows processed per call.
Example use cases
Daily pipeline completeness audit
“I have daily completeness records for my data pipelines — each row has a date, a group name, and observed vs expected data points. Can you run both a numeric distribution and a weighted mean analysis on the observed_points across these 150 rows so I can see where gaps are forming?”
Dataset group coverage comparison
“I want to compare completeness across two dataset groups over the past quarter. Can you take my 90 rows of observed_points and expected_points data and give me a numeric distribution report plus a secondary-measure weighted mean, grouped by dataset_group?”
Monitoring data quality for ETL feeds
“We track expected vs observed data points for each ETL feed daily. Can you run a numeric distribution and weighted mean analysis on the expected_points field for my 200-row completeness log so I can flag underperforming feeds?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-082?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness records with observed and expected point counts and need both a numeric distribution summary and a weighted mean in a single low-cost call ($0.01 USDC for two reports). It is ideal for offline or batch analytical workflows on up to 200 rows where no external data lookup, prediction, or real-time monitoring is needed — purely local computation on supplied data.
Side effects
- Submitting more than 200 rows returns an error or truncated results
- Missing required fields (row_id, date, observed_points, expected_points) cause validation failures
- Invalid date format (not YYYY-MM-DD) triggers schema rejection
- Null values in both numeric fields may skew or invalidate weighted mean computation
- Malformed JSON payload results in a 400-level error
- Payment failure or insufficient USDC balance prevents the call from executing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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