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Dependency Upgrades Trailing Mean + IQR Outlier Analysis
x402
Dependency Upgrades Trailing Mean + IQR Outlier Analysis is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Computes trailing mean time series and IQR-based outlier flags for dependency upgrade metrics (packages_checked, packages_updated) across up to 200 supplied rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dependency upgrades — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied dependency upgrades records (packages_checked, packages_updated). 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
Two independent analytical reports covering: (1) a trailing mean time series computed over the specified rolling window for the chosen field (packages_checked or packages_updated), and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range bounds. Results are derived entirely from the supplied data rows with no external lookups.
Example use cases
Spike detection in CI dependency scans
“I have 90 days of daily CI logs with packages_checked and packages_updated per component. Run a trailing mean with a window of 7 on packages_checked and flag any IQR outliers so I can see which days had unusual scan activity.”
Monthly package update anomaly review
“Here are 150 rows of monthly dependency upgrade records across our microservices — can you compute the trailing mean series with a window of 3 for packages_updated and flag outlier months using IQR?”
Cross-team upgrade cadence comparison
“I want to spot anomalies in how often different teams update packages. Use a window size of 5 on packages_updated across these 60 rows of team-level upgrade logs and give me the trailing mean and IQR outlier flags.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-software-09-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw dependency upgrade tabular data (packages_checked or packages_updated) and need both a rolling/trailing mean trend series and IQR-based outlier detection in a single call for up to 200 rows, without needing live data lookup, prediction, or external enrichment. Prefer it over general-purpose stats APIs when your domain is specifically software dependency upgrade metrics and you want two bundled analytical reports at low cost ($0.01 USDC).
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Window size larger than number of rows — insufficient data for trailing mean
- Missing required fields (row_id, date, component, packages_checked, packages_updated) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Field enum value not 'packages_checked' or 'packages_updated' — rejected
- All values null — IQR computation not possible
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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