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Time Series Completeness — Category Frequencies + IQR Outlier Flags
x402
Time Series Completeness — Category Frequencies + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Analyzes time series completeness records (observed vs expected data points) to produce category frequency distributions and IQR-based outlier flags across up to 200 rows in two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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 category frequency breakdown of the selected field (observed_points or expected_points) grouped by dataset_group, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile fence. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Audit sensor data completeness by group
“I have 90 rows of daily completeness records across three sensor groups — each row has a date, observed_points, and expected_points. Can you run category frequency analysis and IQR outlier detection on observed_points so I can see which groups and dates are anomalous?”
Detect ETL pipeline data gaps
“My ETL pipeline logs show observed_points versus expected_points for each day across dataset groups 'raw' and 'processed' — can you check the expected_points field for IQR outliers and give me a frequency breakdown by category for about 150 rows?”
QA dashboard for time series ingestion
“I want to QA our data ingestion: here are 200 rows with row_id, dataset_group, date, observed_points, and expected_points. Run both the category frequencies and IQR outlier flagging reports on observed_points so I can spot which groups are consistently underperforming.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-107?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cost-effective statistical summary of time series completeness data — specifically IQR outlier detection and category frequency counts — without requiring live data lookups, predictions, or professional assessments. Ideal for batch QA workflows processing up to 200 completeness records at a time where both frequency and outlier reports are needed together.
Side effects
- Fewer than 1 row supplied — minItems constraint violated
- Row missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD pattern — schema rejection
- More than 200 rows supplied — maxItems constraint exceeded
- Non-numeric values in observed_points or expected_points when not null — type error
- Invalid field enum value (not 'observed_points' or 'expected_points') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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