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Data Freshness Trailing Mean Series & Secondary-Measure Weighted Mean
x402
Data Freshness Trailing Mean Series & Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Computes a trailing mean series and a record-count-weighted mean for supplied data freshness records (age_minutes, record_count) across up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Trailing mean series + Secondary-measure weighted mean: Trailing mean series + Secondary-measure weighted mean 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
Two independent analytical reports: (1) a trailing mean series computed over the specified window for the chosen field (age_minutes or record_count), and (2) a record-count-weighted mean for the supplied data freshness rows — both derived from the user-supplied dataset without any live lookup or external data.
Example use cases
Pipeline freshness trend monitoring
“I need to analyze how stale my data pipelines have been getting — I have 120 rows of daily freshness records with age_minutes and record_count per dataset group. Can you run a trailing mean with a window of 5 and also give me the record-count-weighted mean for age_minutes?”
Data quality reporting for multiple groups
“For my weekly data quality report, can you compute both a rolling 10-period trailing mean series and a weighted mean (weighted by record_count) for the age_minutes field across my dataset groups? I'll send you 80 rows of freshness data.”
Detecting freshness degradation over time
“I want to see if my data is getting progressively staler — can you calculate a trailing mean series over a window of 3 and a secondary-measure weighted mean for age_minutes using my 40 rows of freshness records with dates, record counts, and dataset group labels?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-151?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a smoothed rolling/trailing mean time series AND a weighted mean in a single API call for data freshness metrics (age_minutes and record_count). It is purpose-built for data quality and pipeline freshness analytics on batches up to 200 rows, with no external lookups or predictions involved. Prefer this over general-purpose stats APIs when your data is specifically structured around dataset group freshness monitoring.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Window size outside range 2–200 — validation error
- Null values in age_minutes or record_count may affect weighted mean calculation
- Field value not one of the allowed enum values (age_minutes, record_count) — schema error
- Missing required fields (row_id, dataset_group, date) — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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