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Time Series Completeness Audit: Record Quality, String Normalisation & Numeric Interval Filter
x402
Time Series Completeness Audit: Record Quality, String Normalisation & Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Audits time series completeness records (observed_points vs expected_points) with three independent reports: record quality audit, string normalisation, and numeric interval filtering — all processed locally without external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter 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 for up to 200 submitted rows: (1) a record quality audit highlighting malformed or problematic records, (2) a string normalisation report on text fields such as dataset_group, and (3) a numeric interval filter report identifying rows where the selected numeric field (observed_points or expected_points) falls outside the specified minimum/maximum range. All processing is local with no external lookups or predictions.
Example use cases
Daily sensor completeness data audit
“I have daily sensor readings for October 2026 with observed_points and expected_points per dataset group. Can you run all three reports — record quality audit, string normalisation, and numeric interval filter — on up to 80 rows, filtering observed_points between 0 and 500?”
Flagging out-of-range expected data points
“For my time series completeness records across groups 'sales-eu' and 'sales-us' from last month, run the numeric interval filter on expected_points between 10 and 1000, plus the record quality audit and string normalisation, across these 120 rows.”
Normalising dataset group labels in time series
“I've got 150 rows of time series completeness data — date, row_id, dataset_group, observed_points, and expected_points — and I want the string normalisation report to clean up the dataset_group values, alongside a full record quality audit and an interval filter on observed_points from 0 to 250.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, local-only, no-external-lookup batch audit of time series completeness records — specifically when you want all three of record quality audit, string normalisation, and numeric interval filtering in a single $0.01 call for up to 200 rows. Prefer it over general-purpose data tools when your data involves observed_points and expected_points paired by date and dataset_group, and you need structured per-row findings without any live data enrichment or prediction.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, observed_points, expected_points) cause row-level validation errors
- Date strings not matching YYYY-MM-DD pattern are flagged as malformed
- Minimum value exceeds maximum value causing undefined interval filter behaviour
- Numeric field values outside ±1,000,000,000,000 are rejected by schema
- Empty rows array (minItems:1 not met) causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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