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Time Series Completeness — Adjacent Row Changes + Equal-Width Histogram
x402
Time Series Completeness — Adjacent Row Changes + Equal-Width Histogram 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 (observed vs expected points) by computing adjacent row changes and an equal-width histogram in a single API call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Adjacent row changes + Equal-width histogram: Adjacent row changes + Equal-width histogram 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) an adjacent row changes report showing how observed or expected point counts shift between consecutive rows, and (2) an equal-width histogram with configurable bins (1–30) summarizing the distribution of the selected field across all supplied rows. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Daily pipeline completeness gap detection
“I have 30 days of daily completeness records for my ETL pipeline — each row has observed_points and expected_points. Can you compute the adjacent row changes and a 10-bin histogram on the observed_points field so I can see where the biggest drops happen?”
Multi-group sensor data audit
“We track two dataset groups, 'group-a' and 'group-b', with daily completeness records over the past month. Run the adjacent row changes plus a 5-bin equal-width histogram on expected_points so I can audit which group drifts more from its targets.”
Data quality trend summarization
“Here are 50 rows of time series completeness data for our analytics platform — each row has a date, observed_points, and expected_points. Give me both the row-to-row change analysis and an 8-bin histogram on observed_points so I can summarize quality trends for the weekly review.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-156?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary views of time series completeness data — sequential change detection (adjacent row changes) and distributional summarization (histogram) — in a single API call for up to 200 rows. It is ideal for data quality audits, pipeline monitoring, and completeness trend analysis where you already have the raw completeness records and need fast local analytics without any external data fetching or predictions.
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
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- bins value outside 1–30 range — validation error
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Payment not included or insufficient USDC — 402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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