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Time Series Completeness — Base-100 Series + Date by Category Pivot
x402
Time Series Completeness — Base-100 Series + 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 2026-10-15).
Computes a Base-100 indexed time series and a date-by-category pivot table from supplied time series completeness records (observed vs expected data points), returning two analytical reports in one call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Base-100 series + Date by category pivot: Base-100 series + 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: (1) a Base-100 indexed time series normalizing the selected field (observed_points or expected_points) relative to the specified base row, and (2) a date-by-category pivot table cross-tabulating the field values by date and dataset_group. Both reports are derived from the supplied rows with no external data or live lookups.
Example use cases
Daily pipeline completeness audit
“I have daily observed and expected data point counts for three pipeline groups across the past month. Can you index the observed_points as a base-100 series starting from day 0, and also give me a date-by-category pivot so I can see which groups are falling behind?”
Sensor coverage gap analysis
“I want to check completeness for my IoT sensor feeds — I have observed and expected readings per sensor group by date. Run a base-100 index on expected_points from the first row and pivot the results by date and sensor group so I can spot coverage gaps.”
Reporting dataset validation
“Before I finalize my monthly report, I need to verify how complete my datasets are — I have 50 rows of observed vs expected point counts grouped by dataset category and date. Can you give me both a base-100 normalized series and a date-by-category pivot on the observed_points?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-167?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a normalized base-100 time series view and a date-by-category pivot for data completeness records in a single low-cost call. It is ideal for data engineering and data quality workflows where you are tracking observed vs expected data point counts across multiple groups over time and want two complementary analytical views without writing custom code. Prefer this over generic analytics tools when your data fits within 200 rows and you want instant structured output for completeness auditing.
Side effects
- Exceeding 200 rows causes rejection — keep input to maxItems:200
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- base_index out of range (0–199) or pointing to a non-existent row index causes computation error
- Null values in observed_points or expected_points may affect base-100 indexing accuracy
- Payment failure (insufficient USDC balance or x402 protocol error) returns 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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