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Duplicate Observations Trailing Mean & IQR Outlier Flags
x402
Duplicate Observations Trailing Mean & 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 2026-10-20).
Computes a trailing mean time series and flags IQR-based outliers for duplicate_rows or total_rows observations across up to 200 records in two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied duplicate observations records (duplicate_rows, total_rows). 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 independent analytical reports per call: (1) a trailing mean time series computed over the specified window for the chosen field (duplicate_rows or total_rows), and (2) IQR-based outlier flags marking which observations fall beyond 1.5× the interquartile range — all derived from the supplied input rows without any external lookups.
Example use cases
Data pipeline duplication spike detection
“I'm seeing some suspicious dates in my ETL logs — can you run a trailing mean with a window of 5 and IQR outlier flags on these 30 days of duplicate_rows counts for the 'transactions' dataset group? I want to know which dates had abnormally high duplicates.”
Weekly data quality audit for multiple groups
“Every week I want to check my data quality metrics — here are 50 rows of duplicate_rows and total_rows observations across two dataset groups from the past quarter. Use a window of 4 to compute the trailing mean and flag outliers so I can see where duplication got out of hand.”
Automated alert triage for duplicate anomalies
“I got an alert that duplication rates spiked recently. Can you take these 80 observation records for the 'user_events' group and run IQR outlier detection on total_rows with a window of 7 so I can identify which specific dates are statistically anomalous?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local statistical analysis of duplicate observation records — specifically trailing mean time series and IQR outlier detection — without requiring any external data sources, live lookups, or predictions. It is ideal for data quality monitoring workflows where you supply pre-collected duplicate_rows/total_rows metrics and want two complementary statistical views in a single low-cost call. Prefer it over general-purpose analytics tools when your dataset fits within 200 rows and you want deterministic, reproducible outlier flags based on IQR methodology.
Side effects
- Input array exceeds 200 rows — request will be rejected or truncated
- Window size larger than number of input rows — trailing mean cannot be computed for early rows
- Missing required fields (row_id, date, dataset_group) — validation error returned
- Date strings not matching YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside allowed range (±1 trillion) — rejected by schema validation
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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