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Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & Keyed Dataset Comparison
x402
Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & Keyed Dataset Comparison 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).
Audits time series completeness records for quality, normalises strings, and compares two keyed datasets, producing 3 independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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 3 independent reports: (1) a record quality audit highlighting data issues in the supplied rows, (2) a string normalisation report cleaning dataset_group and other string fields, and (3) a keyed dataset comparison report showing differences between the 'rows' and 'other' datasets, all computed locally without any external lookups.
Example use cases
Sensor data gap detection
“I have two batches of IoT sensor readings for October — each up to 100 rows with date, sensor group, observed point counts and expected point counts. Can you audit them for record quality issues, normalise the group name strings, and compare the two batches to see where they differ?”
Event pipeline completeness check
“My data pipeline produces daily event counts per dataset group and I want to validate completeness — can you run a quality audit and compare this week's observed vs expected points across both the primary and backup pipeline logs, up to 50 rows each?”
Financial data series reconciliation
“I have two keyed datasets of daily financial data observations versus expectations for 80 rows each — please audit both for quality problems, normalise any inconsistent string labels, and give me a comparison report highlighting where observed_points and expected_points diverge.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, string normalisation, and keyed dataset comparison for time series completeness data in a single cost-efficient call (0.01 USDC for all 3 reports). Ideal for data engineers validating pipeline completeness, QA workflows comparing observed vs expected counts across dates and groups, or any offline analytical task on up to 200 rows that does not require live lookups, predictions, or external data sources.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern rejected
- Numeric values out of allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected
- Empty rows array (minItems:1) causes error
- Payment failure via x402 protocol prevents endpoint execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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