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Time Series Completeness Analyzer — Numeric Distribution & Adjacent Row Changes
x402
Time Series Completeness Analyzer — Numeric Distribution & Adjacent Row Changes 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).
Computes numeric distribution statistics and adjacent row-to-row change analysis for time series completeness records (observed vs. expected data points) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric distribution + Adjacent row changes: Numeric distribution + Adjacent row changes 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: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, spread) of the chosen field (observed_points or expected_points) across all supplied rows, and (2) an adjacent row change report showing value deltas between consecutive rows — both computed locally with no external lookups. Covers up to 200 input rows.
Example use cases
Daily pipeline completeness audit
“I've got 60 rows of daily completeness data for my ingestion pipeline — each row has a date, dataset_group, observed_points, and expected_points. Can you run the numeric distribution and adjacent row changes reports on the observed_points field so I can see how data completeness is spread and how it shifts day to day?”
Detecting gaps in sensor data collection
“I have 90 days of sensor data completeness records across two groups, 'sensor-north' and 'sensor-south', with observed and expected point counts per day. Please analyze the expected_points field to show me the numeric distribution and how values change between adjacent rows — I want to spot any sudden drops.”
Monthly reporting completeness QA
“Before I finalize my monthly report, I need to validate completeness across 30 date records for dataset group 'finance-feed'. Run the numeric distribution and adjacent row changes analysis on the observed_points field and tell me if anything looks off.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-077?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured time series completeness data (observed vs. expected point counts) and need two quick local analytical reports — numeric distribution and adjacent row changes — without any live lookups, predictions, or external integrations. It is ideal for lightweight data quality checks on up to 200 rows at minimal cost ($0.01 USDC for both reports).
Side effects
- Missing required fields (row_id, date, observed_points, expected_points) causes validation error
- Submitting more than 200 rows exceeds the maxItems limit and will be rejected
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null values in both observed_points and expected_points may produce incomplete distribution stats
- Selecting a field value outside the enum ('observed_points' or 'expected_points') causes a schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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