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Data Freshness Analytics: Trailing Mean Series + Date-by-Category Pivot
x402
Data Freshness Analytics: Trailing Mean Series + Date-by-Category Pivot 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 moving-mean time series and a date-by-category pivot table for supplied data freshness records (age_minutes, record_count) — two independent analytical reports in one call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data freshness — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot 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 moving-mean time series over the chosen field (age_minutes or record_count) using the specified window size, and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group — both computed locally from the supplied rows with no external data access.
Example use cases
Pipeline freshness trend monitoring
“I have daily freshness logs for two data pipelines — 'etl-core' and 'etl-secondary' — across the past week with age_minutes values. Can you compute a trailing mean series with a window of 4 on age_minutes so I can see if either pipeline is getting staler over time?”
Cross-group freshness pivot for reporting
“Pivot my data freshness records by date and dataset_group for my monthly report — I have about 60 rows across three groups (raw-ingest, transformed, curated) with dates in October 2026, and I want to see how record_count is distributed across groups by day.”
Anomaly investigation via rolling average
“Something looks off in my data freshness metrics for group-b — can you run a trailing mean with a window of 5 on age_minutes for these 30 rows and also give me the date-by-category breakdown so I can spot where the staleness spiked?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-06-152?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 data freshness records with age_minutes and/or record_count values and need both a rolling/trailing mean time series AND a date-by-category pivot in a single low-cost call ($0.01 USDC). Ideal for data engineers or analysts monitoring pipeline freshness across multiple dataset groups over time. Not appropriate if you need live data lookup, forecasting, external enrichment, or more than 200 rows processed.
Side effects
- Fewer rows than the window size yields incomplete or no trailing mean values
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- More than 200 rows submitted exceeds the row limit and is rejected
- Null numeric values in the selected field may produce NaN or skipped entries in the mean series
- Invalid field enum value (not age_minutes or record_count) causes a schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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