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Equipment Runtime Trailing Mean Series
x402
Equipment Runtime Trailing Mean Series 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 a trailing (rolling) mean time series for equipment run_hours or downtime_hours across up to 200 dated machine records using a specified window size.
Price / call$0.01
Success—
StatusUnknown
How this service works
Equipment runtime - Trailing mean series: Trailing mean series for Equipment runtime. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded trailing mean series deliverable.
Output
A trailing mean time series where each row's value is replaced with the rolling average of the selected field (run_hours or downtime_hours) over the specified window of preceding records, grouped or ordered by date and machine, with up to 200 output records.
Example use cases
Smoothing factory floor runtime data
“I have 90 days of daily run_hours data for three factory machines — can you calculate a trailing mean with a window of 14 so I can see smoothed weekly trends over time?”
Downtime trend analysis for maintenance scheduling
“Can you run a rolling mean on the downtime_hours field across my 150 equipment records using a window of 5? I want to spot machines that are trending toward more downtime.”
Equipment performance reporting for operations team
“I need to generate a smoothed run_hours series for our assembly line machines using a trailing window of 10 days — I have about 60 records with dates and machine IDs ready to send.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-analytics-equipment-runtime-rolling?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw time-series equipment runtime or downtime data and need a smoothed rolling average to reduce noise, identify trends, or prepare data for reporting — particularly when working with up to 200 records and needing local numeric processing without external data enrichment or forecasting.
Side effects
- Fewer rows than the window size — insufficient data to compute trailing means for early records
- Invalid date format — dates not matching YYYY-MM-DD pattern rejected
- Missing required fields (row_id, machine, date, run_hours, downtime_hours) cause record-level errors
- Exceeding 200 row limit results in request rejection
- Null numeric values may produce null or skipped mean calculations for affected windows
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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