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Record Routing — Trailing Mean Series + IQR Outlier Flags
x402
Record Routing — 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 accepted_records and rejected_records fields in supplied record routing data (up to 200 rows, 2 reports per call).
Price / call$0.01
Success—
StatusUnknown
How this service works
Record routing — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied record routing records (accepted_records, rejected_records). 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 per call: (1) a trailing mean time series computed over the specified rolling window for the selected field (accepted_records or rejected_records), and (2) per-row IQR outlier flags indicating which data points fall outside the interquartile range — all derived entirely from the supplied row data with no external lookups.
Example use cases
ETL pipeline rejection spike detection
“I have 90 rows of daily record routing data from my ETL pipeline — can you compute the trailing mean and flag any IQR outliers in the rejected_records field using a window size of 5, so I can see which days had abnormal rejection spikes?”
Acceptance rate trend monitoring
“Run a trailing mean series with window 10 on my accepted_records data across these 150 rows grouped by dataset region, and flag any outlier rows using IQR — I want to spot where acceptance rates deviated significantly.”
Data quality audit for two record groups
“I've got two dataset groups — 'intake' and 'processed' — with 60 rows of daily counts each. Can you generate the trailing mean and IQR outlier flags for both accepted_records and rejected_records so I can audit which days were anomalous?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-19-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, local statistical analysis — specifically trailing mean smoothing and IQR-based anomaly detection — on record routing metrics (accepted vs. rejected counts) without any external data lookups or predictions. Ideal for ETL monitoring, data quality auditing, and pipeline health checks where you supply your own time-series rows and want two computed reports cheaply ($0.01 USDC). Not suitable if you need forecasting, external data enrichment, or more than 200 rows per call.
Side effects
- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format (must match YYYY-MM-DD pattern)
- Window size outside range 2–200 causes validation error
- Missing required fields (row_id, dataset_group, date, accepted_records, rejected_records) causes rejection
- Numeric values outside ±1,000,000,000,000 range fail schema validation
- Payment of 0.01 USDC not attached or declined — x402 payment required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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