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Time Series Completeness: Trailing Mean Series + Date-by-Category Pivot
x402
Time Series Completeness: Trailing Mean 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 2026-10-19).
Analyzes time series completeness records by generating a trailing mean series and a date-by-category pivot table from supplied observed/expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Trailing mean series + Date by category pivot: Trailing mean 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 trailing mean series showing rolling averages of the selected field (observed_points or expected_points) over the specified window, and (2) a date-by-category pivot table cross-tabulating the selected metric by date and dataset_group — both computed locally from the supplied rows with no external lookups.
Example use cases
Data pipeline completeness audit
“I've got daily records for my ETL pipeline showing how many data points were observed vs expected for two feeds — run a trailing mean with a window of 7 and a date-by-category pivot on observed_points so I can spot where completeness dropped off.”
Sensor coverage trending report
“Here are 150 rows of sensor readings with observed and expected point counts across three sensor groups by date — compute the trailing mean series with window 5 and pivot by date and category on the expected_points field so I can see coverage trends.”
Marketing data quality review
“I need to review completeness across my marketing data feeds — I have 90 days of observed vs expected data points for groups 'email' and 'social'. Give me the trailing mean series with a window of 4 and the date-by-category pivot on observed_points.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-152?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular time series completeness records (observed vs expected data points) and need both a rolling/trailing average trend line and a cross-tabulated pivot by date and category in a single low-cost call. Ideal for data quality monitoring, pipeline audits, and completeness dashboards where you supply your own data and need fast local computation without external lookups or predictions — all for $0.01 USDC covering both reports for up to 200 rows.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds limit
- Window size larger than the number of rows — trailing mean may produce sparse or null results
- Invalid date format (non YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Null values in numeric fields may affect trailing mean calculations
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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