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Time Series Completeness — Grouped Numeric Summary + Category Frequencies
x402
Time Series Completeness — Grouped Numeric Summary + Category Frequencies 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 grouped numeric summaries and category frequency distributions for observed_points and expected_points across dataset groups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Grouped numeric summary + Category frequencies: Grouped numeric summary + Category frequencies 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 grouped numeric summary (e.g. mean, min, max, count, std dev) of the selected numeric field (observed_points or expected_points) broken down by dataset_group, and (2) a category frequency distribution showing how often each dataset_group appears in the supplied records. Covers up to 200 input rows.
Example use cases
IoT sensor coverage gap analysis
“I have daily completeness logs for my IoT sensors across two groups, 'factory-floor' and 'warehouse', from October 2026 — each row has observed_points and expected_points. Can you give me a grouped numeric summary and category frequency breakdown of observed_points so I can see where coverage is worst?”
ETL pipeline data quality audit
“My ETL pipeline logs show observed vs expected data points per pipeline group over the last month — I have about 150 rows with group names like 'orders-pipeline' and 'events-pipeline'. Run both reports (grouped stats + category frequencies) on the expected_points field so I can audit completeness.”
Multi-dataset completeness comparison
“I want to compare completeness across 4 dataset groups in my data lake — each row has a date, row_id, dataset_group, observed_points, and expected_points. Can you summarize the observed_points field with a grouped numeric summary and show me the category frequency distribution across those groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-086?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness records (observed vs expected data points) grouped by dataset category and need both a statistical numeric summary and a frequency count of categories — all in one $0.01 call. Ideal for offline/batch data quality audits where no live lookup or external data is needed, and when your dataset is 200 rows or fewer.
Side effects
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format (must match YYYY-MM-DD pattern) causes schema rejection
- Numeric values outside the allowed range (-1e12 to 1e12) are rejected
- Empty rows array (minItems:1 not met) returns an error
- Invalid field enum value (not 'observed_points' or 'expected_points') fails validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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