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Time Series Completeness — Category Frequencies + Date by Category Pivot
x402
Time Series Completeness — Category Frequencies + 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).
Analyzes time series completeness records by computing category frequencies and a date-by-category pivot table for observed vs. expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Category frequencies + Date by category pivot: Category frequencies + 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 category frequencies table showing how often each dataset_group appears in the supplied records, and (2) a date-by-category pivot table showing the selected field (observed_points or expected_points) broken down by date and dataset_group — all computed locally on the supplied data, up to 200 rows.
Example use cases
IoT sensor coverage audit
“I have daily sensor readings for three device groups — alpha, beta, and gamma — from 2025-01-01 to 2025-03-31, with observed_points and expected_points for each day. Can you give me a category frequency report and a date-by-group pivot so I can see which groups are consistently under-reporting?”
ETL pipeline completeness check
“My ETL pipeline tracks expected vs actual data points loaded each day across pipelines 'crm-sync', 'inventory-feed', and 'orders-feed' for the past two weeks. Can you pivot those completeness records by date and pipeline and also show me the frequency breakdown by pipeline?”
Multi-dataset monitoring summary
“I've got time series completeness data for four dataset groups over the last month — each row has a date, group name, observed_points, and expected_points. I want a category frequency table showing how often each group appears, plus a date-by-category pivot for the expected_points field, so I can spot any gaps.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-110?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular time series completeness data (observed vs. expected points) segmented by category/group and date, and need both a frequency distribution of categories and a date-by-category pivot in a single low-cost call. Ideal for lightweight, offline analytical summarization of up to 200 rows without needing an external BI tool or database.
Side effects
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Submitting more than 200 rows exceeds the row limit and returns an error
- Null values in observed_points or expected_points may result in gaps in pivot output
- Empty rows array returns an error due to minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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