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Time Series Completeness — String Normalisation + Grouped Numeric Summary
x402
Time Series Completeness — String Normalisation + Grouped Numeric Summary 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).
Normalises dataset_group strings and produces grouped numeric summary statistics for time series completeness records (observed_points vs expected_points) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Grouped numeric summary: String normalisation + Grouped numeric summary for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two reports: (1) a string normalisation report for the dataset_group column, and (2) a grouped numeric summary of the selected field (observed_points or expected_points), including aggregated statistics per group — all computed locally from the supplied rows, with no external lookups or predictions.
Example use cases
Daily pipeline completeness audit
“I've got 150 rows of daily sensor data for October, each with a dataset_group, date, observed_points, and expected_points. Can you normalise the group names and give me a grouped numeric summary of observed_points so I can see where completeness is lowest?”
Multi-team data ingestion review
“We have time series completeness records across 5 teams — each row has a team name as dataset_group plus observed and expected point counts. Please normalise the team name strings and produce a grouped numeric summary on expected_points for all 80 rows.”
Completeness regression check after ETL change
“After our ETL update, I want to check whether observed_points changed across our dataset groups. Here are 200 rows with dates, group labels, and both observed and expected counts — normalise the group strings and summarise the observed_points by group so I can spot any regressions.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-023?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation of group labels and grouped numeric summarisation of time series completeness data (observed vs expected points) in a single cheap call ($0.01 USDC for 2 reports). Ideal for data quality pipelines, completeness audits, and ETL monitoring workflows where you have up to 200 rows and need offline, deterministic analytics without any external lookup or ML inference.
Side effects
- More than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date string not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range — rejection
- Invalid field enum value — error response
- Empty rows array — schema minimum violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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