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Time Series Completeness: Weighted Mean + Date-by-Category Pivot
x402
Time Series Completeness: Weighted Mean + 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 secondary-measure weighted mean and a date-by-category pivot table for up to 200 time series completeness records (observed_points vs expected_points) in a single call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Secondary-measure weighted mean + Date by category pivot: Secondary-measure weighted mean + 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 secondary-measure weighted mean of the selected field (observed_points or expected_points) across the supplied rows, and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group — both computed locally without external lookups, covering up to 200 input rows.
Example use cases
Daily pipeline completeness report
“I'm tracking data pipeline completeness for 'pipeline-alpha' and 'pipeline-beta' across the last 30 days. Each row has a date, observed_points, and expected_points. Can you compute the weighted mean and a date-by-group pivot table on the expected_points field so I can see where coverage drops?”
Sensor coverage gap analysis
“We have completeness records for three sensor groups — 'region-north', 'region-south', and 'region-east' — over the past two weeks, with observed and expected point counts per day. Run a secondary-measure weighted mean plus a date-by-category pivot on observed_points so I can spot coverage gaps by region and date.”
ETL job monitoring summary
“I need to review completeness for our ETL jobs across 'batch-1' and 'batch-2' dataset groups for October. I have about 60 rows with date, observed_points, and expected_points. Give me the weighted mean and a pivot of observed_points by date and group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-185?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a weighted mean aggregation and a date-by-category pivot on time series completeness data (observed vs expected points) in a single cheap call ($0.01 USDC). Ideal for lightweight, local analytics on up to 200 rows without needing external data sources, predictions, or ML models. Best suited for data quality monitoring, pipeline completeness dashboards, and coverage reporting scenarios where you control the input data.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null values in observed_points or expected_points may affect weighted mean calculation
- Invalid field enum value (not 'observed_points' or 'expected_points') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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