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Time Series Completeness Analytics: Grouped Numeric Summary + Date-by-Category Pivot
x402
Time Series Completeness Analytics: Grouped Numeric Summary + 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 (observed vs expected data points) by producing two local analytical reports: a grouped numeric summary and a date-by-category pivot, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Grouped numeric summary + Date by category pivot: Grouped numeric summary + 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 from the supplied rows: (1) a grouped numeric summary with aggregate statistics on observed_points or expected_points broken down by dataset_group, and (2) a date-by-category pivot table showing the selected field values cross-tabulated by date and dataset_group. No external lookups or predictions are made.
Example use cases
IoT sensor gap detection by device group
“I have daily sensor readings for two device groups across the past week — each row has a date, a group name like 'sensors-east' or 'sensors-west', and the observed vs expected data points. Can you generate the grouped numeric summary and date-by-category pivot so I can see which groups and dates are underperforming?”
ETL pipeline completeness audit
“Our ETL pipeline tracks observed vs expected records per pipeline category each day. I've got about 150 rows covering October with columns for row_id, dataset_group, date, observed_points, and expected_points. Run both the grouped summary and date-pivot reports on this data using expected_points as the field.”
Multi-dataset monitoring completeness review
“I need to review completeness across our five data feeds for the past month — I have rows with a date, a feed group name, and observed vs expected point counts. Can you produce the grouped numeric summary and date-by-category pivot for all 180 rows so I can spot which feeds and dates have the worst coverage?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-097?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular time series completeness data (observed vs expected points) that you want to summarize and pivot without any external data lookups, predictions, or real-time processing. It is ideal for offline or batch analytical passes on up to 200 rows where you need both a grouped statistical summary and a date-by-category pivot in a single low-cost call ($0.01 USDC for both reports).
Side effects
- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows supplied — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) in any row
- Invalid date format (must be YYYY-MM-DD)
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Invalid field enum value — must be 'observed_points' or 'expected_points'
- Payment failure — insufficient USDC for x402 transaction
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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