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Feature Availability Analytics: Trailing Mean Series + Two-Measure Correlation
x402
Feature Availability Analytics: Trailing Mean Series + Two-Measure Correlation 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 mean time series and a two-measure correlation report for supplied feature availability data (populated_features vs expected_features) over up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation 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 analytical reports sharing local computation engines: (1) a trailing mean time series for the selected measure (populated_features or expected_features) over the specified window, and (2) a correlation report between populated_features and expected_features across the supplied rows. No external data is fetched; all analysis is performed on the submitted rows.
Example use cases
ML pipeline feature completeness monitoring
“I have daily records of how many features were populated vs expected in my ML pipeline — can you run a trailing mean with a window of 14 and a correlation between populated and expected features across these 120 rows to see if my data completeness is trending up or down?”
Dataset group coverage comparison
“For each of my two dataset groups I have 90 rows of populated_features and expected_features by date — compute a rolling mean over a window of 5 and correlate the two measures so I can see which group has better feature coverage.”
Feature store quality audit
“I want to audit my feature store's fill rate — I have 200 date-stamped rows with populated_features and expected_features values. Can you calculate the trailing mean series using a window of 10 and also give me the correlation between the two measures?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-150?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular feature availability records with date, group, and two numeric measures (populated vs expected features) and need both a rolling average trend and a correlation between the two measures in a single $0.01 call. It is ideal for lightweight, offline analytics on up to 200 rows without needing a full data warehouse or external ML service.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Window size larger than the number of rows — trailing mean may be undefined or partially computed
- Missing required fields (row_id, date, populated_features, expected_features) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null values in measure columns may reduce correlation sample size or produce NaN results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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