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Held-Order Release Share Rolling Mean Report
x402
Held-Order Release Share Rolling Mean Report is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Computes a rolling window mean of held-order release share ratios from supplied retail order data, returning up to 200 smoothed values.
Price / call$0.01
Success—
StatusUnknown
How this service works
Held-order release share - Rolling mean report: Rolling mean report for Held-order release share. Supplied retail figures only, up to 200 records. No platform access, live rates or order execution. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded rolling mean report report.
Output
Returns a series of rolling mean values for the held-order release share metric, computed over successive windows of the supplied rows. Each result corresponds to a full-window trailing mean of the release share ratio (released_orders / held_orders), covering up to 200 input records.
Example use cases
Smoothing retail fulfillment rate trends
“I have a series of weekly retail figures showing held orders and released orders — can you run a rolling mean with a window of 3 across all 12 weeks so I can see the smoothed release share trend?”
Detecting order release share dip
“We've had some erratic held-order release numbers over the past 8 periods. Can you compute a rolling mean with a window of 4 on these figures so I can spot where the release share actually started declining?”
Reporting smoothed release share to stakeholders
“For our monthly retail ops report, I need a rolling mean of held-order release share across 20 observations using a window of 5 — can you calculate that from the data I'll provide?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-retail-held-order-release-rolling?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a smoothed, rolling-window view of retail held-order release share across an ordered time series of observations. It is ideal for trend analysis, noise reduction in order fulfillment reporting, and preparing smoothed metrics for dashboards or stakeholder reports. Prefer it over single-point calculations when you have multiple sequential observations and want to surface underlying patterns rather than raw fluctuations.
Side effects
- Fewer than 2 rows supplied — minimum input requirement not met
- Window size larger than number of rows — cannot form a full window
- held_orders value of zero — division by zero in share calculation may cause error or undefined output
- More than 200 rows supplied — exceeds maxItems limit
- Non-numeric or missing required fields in row objects — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| window | integer |
Tags
x402
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