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Data Freshness Analytics: Trailing Mean Series + Two-Measure Correlation
x402
Data Freshness 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 2026-10-22).
Computes a trailing mean time series and a correlation analysis between age_minutes and record_count for supplied data freshness records, returning two statistical reports in one call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied data freshness records (age_minutes, record_count). 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 statistical reports: (1) a trailing mean time series for the selected field (age_minutes or record_count) over the specified rolling window, and (2) a correlation analysis between age_minutes and record_count across the supplied rows — all computed locally from the provided data with no external lookups.
Example use cases
Pipeline latency trend smoothing
“I have daily records of how stale our data pipelines are — each row has a date, age_minutes, and record_count. Can you calculate a 7-day trailing mean for age_minutes and also tell me how age and record count correlate? I have about 90 rows across two dataset groups.”
Data volume and freshness correlation
“Can you analyze whether there's a correlation between age_minutes and record_count in my freshness dataset? I'll give you 60 rows with dates and group labels, and I'd also like the trailing mean series with a window of 3 for age_minutes.”
Cross-group freshness monitoring report
“I'm monitoring data freshness across two dataset groups and want both a smoothed trailing mean series (window of 10) for age_minutes and a correlation report between age_minutes and record_count — here are 120 rows of daily observations, each tagged with a row_id, date, and dataset_group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-150?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two lightweight statistical analyses — trailing mean smoothing and bivariate correlation — on data freshness records (age_minutes and record_count) in a single low-cost call. Ideal for data engineering teams monitoring pipeline health without needing live lookups, ML predictions, or external data sources. Best when you already have the records and need fast, local statistical computation on up to 200 rows.
Side effects
- Fewer than 2 rows provided (minItems:1 but meaningful stats require more)
- Window size larger than the number of rows, producing insufficient trailing mean points
- Null values in age_minutes or record_count may affect correlation calculation
- Row count exceeds 200 (maxItems constraint violated)
- Invalid date format not matching YYYY-MM-DD pattern
- Missing required fields (row_id, dataset_group, date) cause schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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