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Feature Availability Analytics: Trailing Mean Series + Equal-Width Histogram
x402
Feature Availability Analytics: Trailing Mean Series + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Computes a trailing moving-average time series and an equal-width histogram over feature availability data (populated_features or expected_features) for up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Trailing mean series + Equal-width histogram: Trailing mean series + Equal-width histogram 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
Two independent analytical reports: (1) a trailing mean time series showing the rolling average of the selected feature field (populated_features or expected_features) over the specified window, and (2) an equal-width histogram binning the feature values into the requested number of bins with frequency counts — both computed locally against the supplied row data, up to 200 rows.
Example use cases
ML pipeline data completeness audit
“I have 120 rows of daily feature availability records across two dataset groups with populated_features and expected_features counts. Can you generate a trailing mean series with a window of 7 and a 10-bin histogram so I can see how feature fill rates are trending and distributed?”
Data drift monitoring dashboard
“Run a trailing mean and histogram report on this feature availability dataset — 80 rows, target field is expected_features, window of 3, and 5 bins. I want to spot any drift in how many features we expect to be populated week over week.”
Dataset group feature coverage comparison
“I've got 200 rows of feature availability data across multiple dataset groups. Can you compute a 14-period trailing mean series and an equal-width histogram with 20 bins on the populated_features column so I can compare coverage across groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-148?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a time-smoothed trend view (trailing mean) and a frequency distribution (histogram) of feature availability or data completeness metrics in a single paid call. It is ideal for ML feature store monitoring, data quality audits, and completeness reporting where you have structured row-level records with populated and expected feature counts by date. Prefer this over general statistics endpoints when you specifically need histogram binning plus rolling mean on feature-availability-shaped data without requiring live data lookups or external enrichment.
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Window size larger than the number of data rows — may produce sparse or empty trailing mean
- Missing required fields (row_id, date, populated_features, expected_features) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Bins parameter outside 1–30 range — rejected
- All values null for the selected field — histogram and mean series may be empty or undefined
- Payment not received or insufficient USDC — 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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