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Ticket Escalations Trailing Mean Series
x402
Ticket Escalations 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 2026-10-21).
Computes a trailing (rolling) mean time series for ticket escalations or ticket counts across teams and dates, returning up to 200 smoothed data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Ticket escalations - Trailing mean series: Trailing mean series for Ticket escalations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded trailing mean series report.
Output
Returns a bounded trailing mean series report: for each input row (up to 200), a smoothed mean value is computed over the specified trailing window for the chosen field (tickets or escalations), keyed by row_id, team, and date. No enrichment, forecasting, or external data — purely local descriptive analytics.
Example use cases
Support team escalation trend smoothing
“I have daily escalation counts for two support teams over the past week — can you compute a 7-day trailing mean on the 'escalations' field so I can see the smoothed trend? Here are the rows: team-alpha and team-beta, dates 2024-01-01 through 2024-01-07.”
Ticket volume rolling average by team
“Take these 10 rows of daily ticket data for group-a and group-b and give me a trailing mean series on the 'tickets' field with a window of 5, so I can understand the rolling average workload across each date.”
Noise reduction for escalation reporting
“Our daily escalation numbers are really spiky — can you run a trailing mean with a window of 4 on this set of 20 escalation records by date and team so the report is smoother for my weekly review?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-ticket-escalations-rolling?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a simple, fast, local rolling/trailing mean computation on ticket or escalation count time series data, with no need for external enrichment, forecasting, or complex statistical modeling. Ideal for smoothing noisy daily support metrics before reporting or visualization, especially when data is already structured by team and date.
Side effects
- Fewer than 'window' rows provided — trailing mean cannot be fully computed for early rows
- Invalid date format (not YYYY-MM-DD) causes validation error
- Field value not in enum ['tickets','escalations'] causes rejection
- More than 200 rows submitted — request exceeds maxItems limit
- Null values in the target field may produce partial or null mean outputs
- Payment failure or insufficient USDC balance blocks the request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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