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Dataset Storage Analytics: Trailing Mean Series + Adjacent Row Changes
x402
Dataset Storage 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 2026-10-19).
Computes trailing moving-average series and adjacent row-to-row change analysis for dataset storage metrics (used_mb, row_count) over up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied dataset storage records (used_mb, row_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 analytical reports: (1) a trailing mean series showing the rolling average of the chosen field (used_mb or row_count) over the specified window size for each row, and (2) adjacent row changes showing the delta between consecutive rows for that field. Results are computed locally from the supplied data with no external lookups.
Example use cases
Storage growth trend monitoring
“I have 150 daily storage snapshots for my datasets with used_mb values — can you compute the trailing mean with a window of 7 and show me where the biggest adjacent row jumps happened?”
Row count change detection
“Run the trailing mean series and adjacent row changes report on my dataset records using row_count as the field and a window of 5 — I want to spot where counts shifted sharply between consecutive days.”
Multi-group storage comparison
“I've got storage records across two dataset groups, group-a and group-b, with dates and used_mb values — can you compute the trailing mean with window 4 and adjacent deltas so I can compare trends?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-146?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 dataset storage records and need both a rolling/trailing mean trend series and adjacent row-to-row change deltas computed together in a single low-cost call. It is purpose-built for storage metric analytics (used_mb, row_count) with no external dependencies, making it ideal for offline/batch analysis of historical storage snapshots.
Side effects
- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format not matching YYYY-MM-DD pattern — rejected by schema
- Window size less than 2 or greater than 200 — out of allowed range
- Missing required fields (row_id, dataset_group, date) on any row — validation error
- Field value not in enum ['used_mb','row_count'] — rejected
- Non-numeric or out-of-range values for used_mb or row_count
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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