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Time Series Completeness — Record Quality Audit + Base-100 Series
x402
Time Series Completeness — Record Quality Audit + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs two analytical reports on time series completeness data: a record quality audit and a base-100 indexed series, for up to 200 rows of observed vs expected point counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + Base-100 series: Record quality audit + Base-100 series 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: (1) a record quality audit that flags completeness anomalies or data issues for each row, and (2) a base-100 indexed series for the chosen field (observed_points or expected_points) anchored to the specified base row, covering up to 200 input rows across dataset groups and dates.
Example use cases
Daily pipeline completeness audit
“I've got a week of daily completeness records for my ETL pipeline — each row has a date, a dataset group, how many data points arrived, and how many were expected. Can you run the record quality audit and base-100 series on them using observed_points, indexed from the first row?”
Multi-group sensor coverage check
“We have two sensor groups collecting readings, and I want to check completeness across 30 days. Here are the rows with observed and expected point counts per day per group — run the quality audit and base-100 index starting from row 0, analyzing observed_points.”
Benchmarking data collection against targets
“Can you take these 100 rows of observed vs expected data point counts across our dataset groups and dates, produce a quality audit flagging any bad records, and index the expected_points field to base 100 starting from row 0?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-012?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a record quality audit and a base-100 indexed time series from the same completeness dataset in a single call. It is ideal for data engineers and analysts who want to validate observed vs expected point counts and benchmark trends without any live lookup or external data enrichment — purely computational, offline analysis of supplied rows.
Side effects
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- More than 200 rows submitted exceeds the batch limit
- base_index value out of range (must be 0–199) causes an error
- Null values in observed_points or expected_points may affect completeness ratio calculations
- Payment failure (insufficient USDC) prevents the call from completing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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