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Procurement Research — Trailing Mean Series + Adjacent Row Changes
x402
Procurement Research — 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 2026-10-15).
Computes a trailing moving average series and adjacent row-to-row changes for procurement records (quoted_amount or quoted_units) in a single API call returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Procurement research — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied procurement research records (quoted_amount, quoted_units). 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 analytical reports derived from the supplied procurement rows: (1) a trailing mean series showing the rolling average of the selected field (quoted_amount or quoted_units) over the specified window, and (2) an adjacent row changes report showing the delta between consecutive rows for the same field. Both reports are computed locally with no external lookups.
Example use cases
Smoothing supplier quote volatility
“I have 50 procurement records with quoted amounts from different supplier groups over the last quarter. Can you compute a trailing moving average with a window of 5 on the quoted_amount field and also show me the change between each consecutive row?”
Detecting unit price jumps across orders
“Run a trailing mean series with a window of 4 and adjacent row changes on my purchase order data — I want to analyze the quoted_units field to spot where unit quantities shifted significantly between orders.”
Comparing procurement trends by company group
“I've got 120 rows of procurement records with company_group, date, row_id, quoted_amount, and quoted_units. Please calculate both the trailing mean series (window of 7) and the adjacent row changes for quoted_amount so I can see the smoothed trend and the day-to-day swings.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-11-146?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local computation of rolling averages and row-to-row change analysis on procurement records (up to 200 rows) without requiring live data lookups, predictions, or external integrations. It is particularly well-suited for batch analytical passes over structured procurement datasets where both trend smoothing (trailing mean) and volatility detection (adjacent changes) are needed simultaneously for a single low-cost call.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Window size larger than number of rows — trailing mean may be undefined or return nulls for early rows
- Missing required fields (row_id, date, company_group) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- quoted_amount or quoted_units values outside allowed range — validation error
- Empty rows array — rejected per minItems:1 constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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