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Time Series Completeness — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series
x402
Time Series Completeness — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series 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).
Runs three local analytical reports on time series completeness data: a record quality audit, exact row deduplication, and a trailing mean series calculation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Exact row deduplication + Trailing mean series: Record quality audit + Exact row deduplication + Trailing mean series for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent reports for the submitted rows (up to 200): (1) a record quality audit flagging anomalies or issues in the input records, (2) an exact row deduplication report identifying identical records, and (3) a trailing mean series computed over the chosen field (observed_points or expected_points) with the specified window size. All processing is done locally with no external lookups.
Example use cases
Daily completeness audit for ingestion pipeline
“I have up to 50 rows of daily completeness records for my data ingestion pipeline — each row has a date, group, and observed vs expected point counts. Can you run the record quality audit, deduplicate any exact duplicates, and give me a trailing mean over expected_points with a window of 5?”
Detecting duplicate records in multi-group series
“We suspect our completeness tracking system is producing duplicate entries across dataset groups. Can you run the exact row deduplication report along with a quality audit on these 80 rows of observed and expected point data?”
Rolling average analysis for completeness monitoring
“For my time series completeness data covering the last month across two dataset groups, can you compute a trailing mean series on the observed_points field using a window of 7, plus run the quality audit and deduplication checks?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-216?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports (quality audit, deduplication, trailing mean) on time series completeness records in a single $0.01 call, especially when working with datasets up to 200 rows and wanting observed_points vs expected_points analysis without any external data lookup or prediction.
Side effects
- Fewer than 1 or more than 200 rows submitted — validation error
- Missing required fields (row_id, date, observed_points, expected_points) — schema validation failure
- Invalid date format (must match YYYY-MM-DD) — pattern validation error
- Window size outside 2–200 range — validation error
- null values in numeric fields may affect trailing mean computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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