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Join Coverage Analytics: Trailing Mean Series + Missing-Value Fill
x402
Join Coverage Analytics: Trailing Mean Series + Missing-Value Fill 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 fills missing values for join coverage records (matched_rows, source_rows) across up to 200 rows in two bundled reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill 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 analytical reports per call: (1) a trailing mean time series for the selected field (matched_rows or source_rows) computed over the specified window size across the supplied rows, and (2) the same dataset with explicit missing-value fills applied using the chosen method (zero, mean, or median). Both reports are computed locally with no external data lookups.
Example use cases
Smoothing noisy pipeline join metrics
“My ETL pipeline produces daily matched_rows and source_rows counts per dataset group and there are big day-to-day swings — can you compute a trailing mean over a window of 5 days for the matched_rows field and fill any nulls with the mean so I can see the smoothed trend?”
Auditing data completeness for join coverage
“I've got 120 rows of join coverage data across several dataset groups with some missing source_rows values — run a trailing mean series with window 3 and fill the missing source_rows using median so I can audit where coverage drops.”
Preparing join metrics for dashboard reporting
“Before I push our weekly join coverage numbers to the dashboard, I need a trailing mean with window 10 on matched_rows plus explicit zero-fill for any missing values — can you process these 80 rows grouped by dataset_group?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both trailing mean smoothing and explicit missing-value imputation for join coverage metrics (matched_rows/source_rows) in a single call at low cost. Ideal for small-to-medium datasets (up to 200 rows) where you need local, deterministic statistical processing without external lookups or predictions.
Side effects
- Input array exceeds 200 rows — request rejected
- Invalid date format (non-YYYY-MM-DD) causes row validation failure
- Window size larger than the number of rows produces incomplete or empty trailing series
- All values null for a field with mean/median fill yields no imputed values
- Missing required fields (row_id, date, dataset_group) cause row-level errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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