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Time Series Completeness — Adjacent Row Changes + Two-Measure Correlation
x402
Time Series Completeness — Adjacent Row Changes + 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 2026-10-15).
Analyzes time series completeness records by computing adjacent row changes and two-measure correlation between observed_points and expected_points for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Adjacent row changes + Two-measure correlation: Adjacent row changes + Two-measure correlation 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 sharing local engines: (1) an adjacent row changes report showing how observed_points or expected_points shift between consecutive rows, and (2) a two-measure correlation report quantifying the statistical relationship between observed_points and expected_points across all supplied rows. Covers up to 200 input rows with no external lookups or live data.
Example use cases
Daily data pipeline completeness audit
“I have 60 days of pipeline completeness records with observed_points and expected_points per day across two groups — can you analyze the adjacent row changes and correlation between those two measures to help me spot where my data collection dropped off?”
Multi-group sensor coverage review
“Can you run an adjacent-row-change and two-measure correlation report on my sensor coverage data? I have 90 rows across groups 'east-region' and 'west-region' with daily observed and expected point counts from 2024-10-01 to 2024-12-29.”
Data quality trend reporting for SLA monitoring
“I need a completeness trend report for our SLA review — please analyze adjacent row changes and correlation between observed_points and expected_points for my 150-row time series dataset covering January through May 2025, grouped by 'production' and 'staging'.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-158?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness records (observed vs expected point counts) and need both a sequential change analysis and a correlation metric in a single low-cost call. It is ideal for data quality pipelines, SLA monitoring, and completeness audits where you already hold the raw rows and want offline statistical summaries without any external data fetching or prediction.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Null values in both measure columns preventing correlation computation
- Single-row input preventing adjacent change computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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