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Time Series Completeness — Record Quality Audit + String Normalisation + Missing-Value Fill
x402
Time Series Completeness — Record Quality Audit + String Normalisation + Missing-Value Fill 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, normalises, and fills missing values in time series completeness records (observed_points vs expected_points) across up to 200 rows, producing 3 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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 analytical reports per call: (1) a record quality audit flagging anomalies and invalid entries, (2) a string normalisation report for text fields like dataset_group, and (3) an explicit missing-value fill report showing imputed values for the chosen field (observed_points or expected_points) using the selected method (zero, mean, or median). All analysis is performed locally with no external lookups.
Example use cases
IoT sensor completeness audit
“I have daily IoT sensor readings for the past 3 months with observed_points and expected_points per device group. Can you audit the record quality, normalise the string fields, and fill any missing observed_points using median imputation across all 90 rows?”
Data pipeline gap detection
“My ETL pipeline logs show completeness records for 5 dataset groups over 40 days — some observed_points values are null. Please run a quality audit and fill those missing values using the mean method so I can see what was imputed.”
Monthly reporting data cleanup
“Before I generate my monthly analytics report, can you audit these 120 rows of completeness records for data quality issues, normalise the group name strings, and fill any missing expected_points values using zero-fill?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined, cost-efficient batch operation that simultaneously audits record quality, normalises string fields, and imputes missing values in time series completeness data — all in one call for up to 200 rows. Ideal when you have observed_points vs expected_points data that needs cleaning before downstream analysis, and you want 3 structured reports without calling separate specialised services.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) causes row-level rejection
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric values outside the allowed range (-1e12 to 1e12) are rejected
- Selecting an invalid imputation method enum value returns a schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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