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Time Series Completeness — Numeric Interval Filter + Secondary-Measure Weighted Mean
x402
Time Series Completeness — Numeric Interval Filter + 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 2026-10-24).
Filters time series completeness records by a numeric interval on one measure, then computes a secondary-measure weighted mean across the filtered rows — returning two analytical reports for up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric interval filter + Secondary-measure weighted mean: Numeric interval filter + 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 derived from the supplied completeness rows: (1) rows filtered to those where the selected field (observed_points or expected_points) falls within the specified numeric interval [minimum, maximum], and (2) a secondary-measure weighted mean computed across those filtered rows. Reports are generated locally from supplied data with no external lookups.
Example use cases
Data pipeline completeness QA
“I have daily completeness logs for two dataset groups across last week — observed_points and expected_points per day — can you filter to only the rows where observed_points falls between 5 and 25, then compute the weighted mean of expected_points across that filtered set and give me both reports?”
Anomalous completeness range investigation
“Here are 50 completeness records for my sensor datasets with observed and expected point counts per day; filter out any row where expected_points is outside the range 10 to 500, then calculate the weighted mean of observed_points for the ones that remain.”
Group-level completeness summary
“I've got completeness data for group-a and group-b spanning 3 months — up to 200 rows total — please filter where observed_points is between 0 and 100, then give me a weighted mean using expected_points as the weight measure, split into the two analytical reports.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-132?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness records (observed vs expected point counts) and need both an interval-filtered subset and a weighted mean computed locally from your supplied data — no external data fetch, prediction, or live lookup required. Ideal for data quality audits, pipeline monitoring summaries, and batch completeness analytics on up to 200 rows at low cost ($0.01 USDC for 2 reports).
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- date values not matching YYYY-MM-DD pattern — validation error
- minimum greater than maximum — undefined or empty filter result
- null values in observed_points or expected_points affecting weighted mean calculation
- row_id missing or exceeding 80 characters — schema validation failure
- field enum value not one of 'observed_points' or 'expected_points' — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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