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Time Series Completeness — String Normalisation + Date by Category Pivot
x402
Time Series Completeness — String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Runs two local analytical reports on time series completeness data: string normalisation and a date-by-category pivot, for up to 200 rows of observed/expected point records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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 analytical reports computed locally on the supplied rows: (1) a string normalisation report cleaning the dataset_group and related string fields, and (2) a date-by-category pivot table showing the selected numeric field (observed_points or expected_points) broken down by date and dataset_group category. No external lookups or predictions are included.
Example use cases
Completeness audit across sensor groups
“I've got daily completeness records for three sensor dataset groups from October 2026, each with observed_points and expected_points. Can you normalise the group name strings and pivot the observed_points values by date and category so I can see where gaps are?”
Pipeline monitoring data cleanup
“Our data pipeline produces completeness logs with dataset_group labels that have inconsistent casing and spacing. Can you run string normalisation on these rows and also give me a date-by-category pivot of the expected_points so I can spot missing data by day?”
Weekly reporting on data ingestion completeness
“Here are 150 rows of our weekly ingestion completeness records with dates, dataset groups, and observed vs expected point counts — please run the two standard reports: string normalisation and a date-by-category pivot on observed_points.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured time series completeness records (with observed and expected point counts per group per date) and need both string normalisation and a date-by-category pivot in a single low-cost call. Ideal for data engineering pipelines, completeness audits, and reporting workflows where you want two bundled local analytical transformations without any live data lookup or external enrichment.
Side effects
- Fewer than 1 or more than 200 rows supplied — validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) on any row — schema rejection
- Invalid date format (must be YYYY-MM-DD) — pattern mismatch error
- Numeric values outside allowed range (-1e12 to 1e12) — validation failure
- Invalid field enum value (must be 'observed_points' or 'expected_points') — rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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