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Time Series Completeness — Record Quality Audit + String Normalisation + Category Frequencies
x402
Time Series Completeness — Record Quality Audit + String Normalisation + Category Frequencies 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 category frequencies) on up to 200 rows of time series completeness data with observed and expected point counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies 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
Three analytical reports: (1) a record quality audit flagging issues like nulls or outliers in the observed/expected point fields, (2) a string normalisation report cleaning and standardising the dataset_group and other string fields, and (3) a category frequencies report summarising the distribution of values across dataset groups and dates — all computed locally without external lookups.
Example use cases
IoT sensor completeness audit
“I have daily readings from two sensor groups — sensor-alpha and sensor-beta — for the past week. Can you run the record quality audit, string normalisation, and category frequencies reports on all 7 rows to find any data quality problems? Each row has a date, group name, observed points, and expected points.”
Pipeline monitoring data review
“We track pipeline data completeness daily and I want three reports — quality audit, string normalisation, and category frequencies — on our last 30 days of records across pipeline-east and pipeline-west groups. Each entry has observed and expected data points for a given date.”
Multi-group completeness frequency check
“Can you tell me how often each dataset group appears and whether any records have quality issues across these 50 completeness log entries? Run the string normalisation, record quality audit, and category frequency summaries on all of them.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-195?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and category frequencies — on a compact dataset of up to 200 time series completeness records in a single low-cost call. Prefer it over separate tooling when you want all three report types at once without external lookups or live data access, and when your data fits the observed_points/expected_points schema.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) causes row-level errors
- Malformed date strings not matching YYYY-MM-DD pattern are rejected
- Null or out-of-range numeric values for observed_points or expected_points may be flagged or rejected
- Payment failure (insufficient USDC balance) prevents the call from completing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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