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Time Series Completeness — Adjacent Row Changes + Secondary-Measure Weighted Mean
x402
Time Series Completeness — Adjacent Row Changes + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Analyzes time series completeness records by computing adjacent row-to-row changes and a secondary-measure weighted mean across up to 200 rows in two independent local reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean for supplied time series completeness records (observed_points, expected_points). 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 computed locally: (1) an adjacent row changes report showing differences between consecutive rows for the selected field, and (2) a secondary-measure weighted mean report aggregating completeness metrics. No external data is fetched; analysis is based entirely on the supplied rows (up to 200).
Example use cases
Daily pipeline completeness drift check
“I have 30 days of completeness records for my ETL pipeline — each row has a date, a dataset group, observed_points, and expected_points. Can you run the adjacent row changes and weighted mean reports on these so I can see where my completeness drifted day over day? Use observed_points as the field.”
Dataset group weighted mean comparison
“Compare the secondary-measure weighted mean across my two dataset groups using these 50 completeness records — dates run from 2025-01-01 to 2025-02-19, with observed_points and expected_points per row. I want both the row change report and the weighted mean using expected_points as the field.”
Monitoring sensor coverage gaps
“I'm tracking sensor coverage completeness for 120 daily readings across three sensor groups. Can you produce the adjacent row changes report and the weighted mean summary for my observed_points column so I can see where gaps are clustering?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-159?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two simultaneous local analytical reports — adjacent row change attribution and a weighted mean — on time series completeness data (observed vs expected points) for up to 200 rows, without any live lookup or external data enrichment. It is ideal for lightweight, self-contained batch analysis of data pipeline or sensor completeness metrics at very low cost ($0.01 USDC).
Side effects
- Fewer than 1 row or more than 200 rows provided — request rejected
- Invalid date format (not YYYY-MM-DD) causes row validation failure
- Missing required fields (row_id, observed_points, expected_points) causes schema error
- Null values in observed_points or expected_points may affect weighted mean calculation
- Invalid field enum value (not 'observed_points' or 'expected_points') causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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