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Feature Availability Analytics — Trailing Mean Series & Date-by-Category Pivot
x402
Feature Availability Analytics — 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 2026-10-16).
Computes a trailing mean time series and a date-by-category pivot table from supplied feature availability records (populated_features, expected_features) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot for supplied feature availability records (populated_features, expected_features). 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 independent analytical reports: (1) a trailing mean time series for the selected field (populated_features or expected_features) computed over the specified rolling window, and (2) a date-by-category pivot table breaking down values by date and dataset_group — both derived entirely from the supplied rows, with no external data lookup.
Example use cases
Data pipeline feature completeness audit
“I have daily feature availability records for two dataset groups over the past month — can you compute a rolling 7-day mean of populated_features and also give me a pivot table showing populated_features by date and group so I can spot which groups are consistently under-populated?”
ML feature store quality monitoring
“My ML feature store tracks populated_features and expected_features per dataset group each day. Run a trailing mean series with window 3 on expected_features and a date-by-category pivot so I can see where feature expectations are drifting over time.”
Weekly data quality report for stakeholders
“I need a quick analytics report on our feature availability logs — about 150 rows covering several dataset groups. Please compute a trailing mean (window 5) on populated_features and a date-by-category pivot table so I can include both charts in the weekly data quality summary.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-152?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of feature availability records and need both a rolling mean time series and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for data quality monitoring, ML feature store auditing, and offline analytics where all data is supplied by the caller and no external lookups or live data sources are needed. Prefer this over general-purpose analytics tools when the specific combination of trailing mean and pivot reports on populated_features/expected_features fields fits your use case.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Window size larger than the number of rows supplied — trailing mean may be empty or partial
- Invalid date format (not YYYY-MM-DD) — row validation failure
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) — schema validation error
- Null values in populated_features or expected_features may produce NaN entries in mean calculation
- Invalid field enum value (not 'populated_features' or 'expected_features') — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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