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Time Series Completeness — Numeric Interval Filter + Date by Category Pivot
x402
Time Series Completeness — Numeric Interval Filter + 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 2026-10-24).
Analyzes time series completeness records (observed vs expected points) using a numeric interval filter and a date-by-category pivot table, delivering two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric interval filter + Date by category pivot: Numeric interval filter + 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 analytical reports: (1) a numeric interval-filtered subset of the input rows where the selected field (observed_points or expected_points) falls within the specified minimum/maximum range, and (2) a date-by-category pivot table aggregating completeness metrics across dataset groups and dates. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Data pipeline completeness audit
“I have daily completeness logs for two data pipelines — group-alpha and group-beta — with observed and expected point counts from the past month. Can you filter rows where expected_points falls between 5 and 50, and also pivot the data by date and group so I can see coverage at a glance?”
Monitoring dashboard completeness slice
“Here are 120 completeness records across four sensor dataset groups. Run a numeric interval filter on observed_points between 10 and 200 to isolate mid-range rows, and give me a date-by-category pivot so I can see which groups had data on which days.”
QA report for time series ingestion
“I'm doing a QA review — I have completeness records with observed vs expected points for three dataset groups over the last quarter. Filter for rows where observed_points is between 0 and 80, and pivot by date and category so I can spot gaps in ingestion.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-133?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary views of time series completeness data in a single paid call: a numeric range filter to isolate rows within a specific observed/expected point band, plus a date-by-category pivot to visualize group coverage over time. It is ideal for data quality audits, pipeline monitoring, and ingestion QA where you already have the completeness records and need fast local analytics without any external data lookup.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- minimum greater than maximum causing empty interval filter result
- Payment not confirmed (x402 protocol) — 402 response before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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