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Data Freshness Analytics — Trailing Mean Series + Adjacent Row Changes
x402
Data Freshness Analytics — Trailing Mean Series + Adjacent Row Changes 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 two analytical reports on data freshness records: a trailing moving-average series and adjacent row-to-row delta changes, for up to 200 rows of age_minutes or record_count data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes 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 independent analytical reports: (1) a trailing mean series showing the rolling average of the selected field (age_minutes or record_count) over the specified window, and (2) adjacent row changes showing the difference between each consecutive row's values. Both reports are computed locally with no external lookups.
Example use cases
Freshness drift trend detection
“I've got a week's worth of daily freshness snapshots for two dataset groups — can you compute a trailing mean series and adjacent row changes for the age_minutes field with a window of 3, so I can see if data is getting staler over time?”
Record count volatility analysis
“Here are 80 rows of ingestion batch records with record_count values — run both a rolling mean with window 7 and adjacent row deltas on the record_count field so I can spot unusual jumps between batches.”
Multi-group freshness comparison
“I have freshness data across several dataset_groups over 30 dates — give me the trailing mean series and adjacent row changes for age_minutes using a window of 4 so I can compare stability across groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-146?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of data freshness records (age_minutes, record_count) and need both a trailing moving-average time series and adjacent row-to-row delta analysis in a single call. It is ideal for lightweight, offline analytical summaries of freshness metrics without requiring a full data warehouse or streaming pipeline. Prefer it over general-purpose analytics tools when cost efficiency ($0.01 for two reports) and deterministic local computation are priorities.
Side effects
- Fewer rows than window size causes incomplete or no trailing mean values at the start of the series
- Missing or null age_minutes/record_count values may produce null deltas or means for affected rows
- Rows exceeding the 200-row limit will be rejected
- Invalid date format (not YYYY-MM-DD) causes validation error
- Empty rows array returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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