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Time Series Completeness Analytics — Quality Audit + String Normalisation + Daily Sum Series
x402
Time Series Completeness Analytics — Quality Audit + String Normalisation + Daily Sum 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 (record quality audit, string normalisation, and daily sum series) on time series completeness data (observed vs expected points) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum 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 analytical reports generated from the supplied rows: (1) a record quality audit flagging issues in the completeness data, (2) a string normalisation report cleaning and standardising dataset_group and other string fields, and (3) a daily sum series aggregating observed_points or expected_points totals by date. All analysis is performed locally with no external lookups or predictions.
Example use cases
Data pipeline completeness gap detection
“I've got daily completeness records for my ETL pipeline — each row has a date, a dataset_group, and the observed vs expected data points for that day. Can you run a quality audit on them and also give me the daily sum series so I can see where the biggest gaps are? Here are the 80 rows.”
Multi-group string normalisation audit
“My dataset groups have inconsistent naming conventions across these 120 time series completeness records. Can you run the string normalisation report on them alongside the full quality audit so I can clean up the group labels? I'll pass you the rows with row_id, dataset_group, date, observed_points, and expected_points.”
Weekly completeness summary for monitoring dashboard
“I need a daily sum series for the expected_points field across all my dataset groups for the past 30 days — here are 30 rows of completeness records. Also run the record quality audit so I know if any rows have issues before I push this to our dashboard.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-198?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need batch analytical reporting on time series completeness records (observed vs expected point counts) and want three coordinated reports — quality audit, string normalisation, and daily sum series — in a single low-cost call. It is ideal for data pipeline monitoring, ETL completeness checks, and dashboard preparation workflows where up to 200 rows need offline analytical processing without any live external lookups or predictions.
Side effects
- Exceeding 200 rows returns a validation error
- Rows missing required fields (row_id, date, observed_points, expected_points) cause schema rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 bounds cause schema rejection
- Empty rows array (minItems:1 violated) returns an error
- Malformed JSON body results in a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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