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Time Series Completeness — Two-Measure Correlation + Date-by-Category Pivot
x402
Time Series Completeness — Two-Measure Correlation + Date-by-Category Pivot 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).
Runs two analytical reports on time series completeness data: a Pearson/correlation analysis between observed_points and expected_points, and a date-by-category pivot table, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Two-measure correlation + Date by category pivot: Two-measure correlation + Date by category pivot 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) a correlation analysis between the observed_points and expected_points columns across all supplied rows, and (2) a pivot table of values (for the selected field, defaulting to observed_points) broken out by date rows and dataset_group columns. Results are based solely on the submitted data with no external lookups or predictions.
Example use cases
Data pipeline completeness audit
“I have daily observed vs expected data point counts for two pipeline groups across the past month — can you correlate observed_points with expected_points and also pivot the results by date and group so I can spot where completeness is breaking down?”
Sensor coverage gap analysis
“Here are 150 rows of sensor reading completeness records with observed_points and expected_points per sensor group and date — run the correlation report and the date-by-category pivot on observed_points so I can see which groups are consistently underreporting.”
ETL monitoring report
“I need to review my ETL job completeness data: each row has a date, a dataset_group, observed_points, and expected_points for about 80 records — please compute the two-measure correlation and give me a pivot of observed_points by date and dataset group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-182?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of time series completeness records with observed and expected point counts and need both a correlation between those two measures and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for lightweight, offline statistical summarization of completeness data without requiring external data sources, prediction services, or professional assessment tools.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) causes a schema rejection
- Malformed date strings not matching YYYY-MM-DD pattern cause validation failure
- Null values in both measure columns may degrade or invalidate correlation output
- Submitting fewer than 1 row causes a minItems validation error
- Payment not included or incorrect USDC amount results in a 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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