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Time Series Completeness — Record Quality Audit + String Normalisation + Equal-Width Histogram
x402
Time Series Completeness — Record Quality Audit + String Normalisation + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Runs three analytical reports (record quality audit, string normalisation, and equal-width histogram) on up to 200 time series completeness records containing observed and expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Equal-width histogram: Record quality audit + String normalisation + Equal-width histogram 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: (1) a record quality audit flagging issues such as nulls, out-of-range values, or malformed entries; (2) a string normalisation report showing how text fields like dataset_group were standardised; and (3) an equal-width histogram of the selected numeric field (observed_points or expected_points) divided into up to 30 bins, all computed locally without any external lookups.
Example use cases
Data pipeline completeness audit
“I have 120 rows of daily completeness data for our ingestion pipeline — each row has a dataset_group, a date, and observed vs expected data point counts. Can you run a full quality audit, normalise the string fields, and give me a 5-bin histogram on the observed_points so I can spot where coverage drops off?”
Detecting anomalies in sensor coverage
“We're tracking how many sensor readings we actually got versus how many we expected each day across different sensor groups. Can you audit those records for quality issues, normalise any inconsistent strings, and show me a 10-bin histogram of the expected_points distribution for about 80 rows?”
Reporting on historical time series gaps
“I want to understand the distribution of data completeness in our historical dataset — I have 200 rows with observed and expected point counts by group and date. Please run the record quality audit and string normalisation, then build me an equal-width histogram with 15 bins on the observed_points field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-202?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three analytical reports in a single low-cost call against supplied time series completeness data — specifically when you want record quality auditing, string field normalisation, and a histogram distribution summary together. It is ideal for offline/batch data validation workflows where no live data lookup or external prediction is needed, and when your dataset fits within 200 rows.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (non-YYYY-MM-DD) causes row rejection
- Bins parameter outside 1–30 range returns a schema validation error
- Null values in observed_points or expected_points may reduce histogram accuracy
- Missing required fields (row_id, date) cause row-level errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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