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Time Series Completeness — Base-100 Series + IQR Outlier Flags
x402
Time Series Completeness — Base-100 Series + 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).
Computes a base-100 normalized series and IQR-based outlier flags for time series completeness data (observed vs. expected points), returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Base-100 series + IQR outlier flags: Base-100 series + 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 base-100 normalized series for the selected field (observed_points or expected_points), rebased to 100 at the specified index, showing relative changes across the time series; and (2) IQR-based outlier flags identifying rows where values fall beyond 1.5× the interquartile range, indicating anomalous completeness observations.
Example use cases
Daily pipeline completeness anomaly detection
“I have daily completeness records for two ETL pipelines — group-a and group-b — with observed and expected data points from the past month. Can you normalize the observed_points field to a base-100 series starting at index 0 and flag any IQR outliers so I can see which days had abnormal completeness?”
Sensor coverage baseline comparison
“I'm tracking sensor data coverage across multiple stations over 60 days — each row has an observed_points count and an expected_points count. Can you run the base-100 series and IQR outlier analysis on expected_points using index 0 as the baseline so I can spot coverage gaps?”
Data warehouse ingestion quality audit
“We have 150 rows of daily ingestion completeness records across several dataset groups with observed and expected point counts per day. Can you produce a base-100 indexed series on observed_points starting at row index 0 and generate the IQR outlier flags to identify which days had suspicious data volumes?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-164?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness records (observed vs. expected data points) and need both a base-100 normalized trend view and IQR-based outlier detection in a single call. It is ideal for data quality monitoring, pipeline auditing, or coverage analysis where you already have the data locally and need fast, offline statistical computation without external lookups or predictions. Best for datasets up to 200 rows where two simultaneous analytical reports (normalization + outlier flagging) are needed at low cost.
Side effects
- Fewer than 1 or more than 200 rows provided — returns validation error
- Invalid date format (not YYYY-MM-DD) — returns schema validation error
- base_index out of range (0–199) or exceeds actual row count — returns error
- Null values in observed_points/expected_points may affect IQR and base-100 calculations
- Field value not in enum ['observed_points','expected_points'] — returns validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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