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Time Series Completeness Analytics — Record Quality Audit, String Normalisation & Weighted Mean
x402
Time Series Completeness Analytics — Record Quality Audit, String Normalisation & Weighted Mean 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 (observed vs expected points): record quality audit, string normalisation, and secondary-measure weighted mean — up to 200 rows per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean 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 computed locally: (1) a record quality audit identifying issues in the completeness records, (2) a string normalisation report for text fields such as dataset_group, and (3) a secondary-measure weighted mean computed over the selected field (observed_points or expected_points) across up to 200 input rows. No external data is fetched or predictions made.
Example use cases
Data pipeline completeness health check
“I need to audit the quality of my data pipeline completeness logs — I have 120 rows with date, dataset_group, observed_points and expected_points columns. Can you run the record quality audit, string normalisation, and weighted mean reports on them, using observed_points as the field?”
Sensor feed gap detection audit
“My IoT sensor feeds have been inconsistent lately. Can you analyse these 80 completeness records — each has a row_id, a sensor group name, a date, and observed vs expected data point counts — and give me the quality audit, normalised strings, and a weighted mean summary?”
Monthly reporting data quality review
“Before I send out our monthly data completeness report, can you run the three analytics — quality audit, string normalisation, and secondary-measure weighted mean — on these 200 rows of completeness records with observed_points and expected_points for each dataset group and date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled analytical operations (quality audit, string normalisation, weighted mean) on time series completeness data in a single low-cost call ($0.01 USDC for all three reports). Ideal when you already have observed vs expected point counts per date and dataset group and want offline/local analysis without any live lookups, predictions, or external integrations.
Side effects
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) — rejected
- Invalid field enum value (must be 'observed_points' or 'expected_points') — schema error
- Payment not included or insufficient — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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