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Null Coverage Analytics — Trailing Mean Series + Two-Measure Correlation
x402
Null Coverage Analytics — 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 2026-10-18).
Computes a trailing moving-average time series and a Pearson/two-measure correlation between missing_values and total_values for up to 200 supplied null-coverage records, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied null coverage records (missing_values, total_values). 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: (1) a trailing moving-average time series over the selected field (missing_values or total_values) using the specified window size, and (2) a two-measure correlation analysis between missing_values and total_values — all computed locally from the supplied rows, with no external data lookups. Up to 200 rows supported per call.
Example use cases
Data pipeline null rate trend analysis
“I have 30 days of null coverage records for my ETL pipeline — each row has a date, missing_values, and total_values. Can you run a trailing mean series with a window of 7 on missing_values and also show me the correlation between missing_values and total_values across all rows?”
Multi-group data quality comparison
“I've got 60 rows of null coverage data split between two dataset groups, 'warehouse' and 'staging', with dates from 2024-03-01 onward. Run a 10-window trailing mean on total_values and give me the two-measure correlation report for missing_values vs total_values.”
Weekly data completeness reporting
“Here are my weekly null coverage numbers for the last 20 weeks — row IDs, dates, missing_values, and total_values all included. Compute the trailing mean series with a window of 4 and correlate missing_values with total_values so I can include the stats in my data quality summary.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-150?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a rolling/trailing mean time series AND a correlation statistic between two null-coverage measures (missing_values and total_values) in a single low-cost call. It is ideal for data quality monitoring workflows where you want to smooth noisy null-rate data over time and simultaneously understand the linear relationship between missingness and dataset size. Prefer it over general-purpose analytics when your data follows the null-coverage schema (row_id, dataset_group, date, missing_values, total_values) and you need both reports bundled at $0.01 USDC.
Side effects
- Fewer than 2 rows supplied — minimum 1 row required but trailing mean needs at least window-size rows to produce meaningful output
- Window size larger than the number of rows — leading to empty or truncated trailing mean series
- Missing required fields (row_id, date, missing_values, total_values) — validation error
- Date strings not matching YYYY-MM-DD pattern — schema rejection
- Rows exceed 200 maximum — request rejected
- Null/missing numeric values in critical fields — may produce NaN or skipped entries in the series
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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