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Label Review Analytics — Trailing Mean Series + IQR Outlier Flags
x402
Label Review Analytics — Trailing Mean Series + IQR Outlier Flags 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 trailing mean time-series and IQR-based outlier flags for label review records (reviewed_records and changed_labels) across up to 200 rows, producing two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied label review records (reviewed_records, changed_labels). 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 for up to 200 input rows: (1) a trailing mean time series computed over a configurable window for the selected field (reviewed_records or changed_labels), and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range bounds — both derived entirely from the supplied data with no external lookups.
Example use cases
Detect anomalous annotation days
“I have 120 rows of daily label review logs with reviewed_records and changed_labels across three dataset groups — can you compute a trailing mean with a window of 5 and flag any IQR outliers in the changed_labels field so I can spot days where label edits were unusually high?”
Monitor labeling throughput trends
“Run a trailing mean series with window size 10 on my reviewed_records column from the past quarter of annotation sessions, and flag any outlier days using IQR — I want to see where throughput dropped or spiked unexpectedly.”
Audit label quality by group
“I've got 80 rows of label review records split across four dataset groups and I need two reports: a trailing mean series and IQR outlier flags, using a window of 3, focused on reviewed_records — can you run that analysis for me?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a smoothed trailing mean time series and IQR-based outlier detection on label review metrics (reviewed_records or changed_labels) in a single low-cost call. It is ideal for small-to-medium datasets (up to 200 rows) where you want self-contained statistical analysis without any external data lookups or model inference. Prefer it over general-purpose analytics tools when your data is already structured in the expected schema and you want a fast, deterministic, local computation result for annotation quality monitoring.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, reviewed_records, changed_labels) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Window size smaller than 2 or larger than 200 — parameter error
- Numeric values outside allowed range (-1e12 to 1e12) — validation rejection
- Fewer rows than the specified window size — trailing mean may be undefined for early rows
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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