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Join Coverage — Trailing Mean Series + Two-Measure Correlation
x402
Join Coverage — Trailing Mean Series + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes a trailing moving average series and a two-measure (matched_rows vs source_rows) Pearson correlation for supplied join coverage row data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied join coverage records (matched_rows, source_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: (1) a trailing mean series showing the rolling average of the selected field (matched_rows or source_rows) over the specified window, computed across the supplied time-ordered rows; and (2) a two-measure correlation report quantifying the statistical relationship between matched_rows and source_rows across the supplied dataset. Both reports are computed locally with no external lookups.
Example use cases
ETL pipeline join quality trend
“I have daily join coverage records for two dataset groups over the past month — can you compute a trailing mean series with window 7 on matched_rows and also give me the correlation between matched_rows and source_rows across all those rows?”
Data audit: match rate smoothing
“I'm auditing our data ingestion joins. Here are 50 rows with date, dataset_group, matched_rows, and source_rows. Please calculate a rolling mean over a window of 5 for the source_rows field and show me how matched_rows and source_rows correlate.”
Detecting join degradation over time
“Can you run a trailing mean with window 4 on matched_rows for these join coverage records and also compute the two-measure correlation so I can see if matched and source row counts are drifting apart? I have about 30 rows across two groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-150?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a rolling/trailing mean time series and a correlation between matched_rows and source_rows for join coverage data in a single paid call. It is ideal for data engineers auditing ETL join quality, teams monitoring dataset match rates over time, or analysts who want smoothed trend lines alongside a correlation metric without writing custom statistical code. Prefer it over general-purpose analytics tools when working specifically with join coverage records (matched_rows, source_rows) and needing both reports in one low-cost micro-payment call.
Side effects
- Fewer than minimum required rows (minItems: 1) returns a validation error
- Window size larger than number of supplied rows may produce incomplete or null trailing values
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Row count exceeding 200 items is rejected by the schema
- Null values in matched_rows or source_rows may reduce effective sample size for correlation
- Missing required fields (row_id, dataset_group, date) cause input validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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