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Time Series Completeness Analyzer: String Normalisation + IQR Outlier Flags
x402
Time Series Completeness Analyzer: String Normalisation + 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 2026-10-15).
Performs string normalisation and IQR-based outlier detection on up to 200 rows of time series completeness records (observed_points vs expected_points), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + IQR outlier flags: String normalisation + 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 independent analytical reports: one covering string normalisation of the input records and one covering IQR-based outlier flags on the selected numeric field (observed_points or expected_points). Each report summarises anomalies and normalised values across up to 200 input rows.
Example use cases
Data pipeline completeness audit
“I have daily completeness records for two dataset groups over the past 3 months — observed_points and expected_points per day. Can you normalise the strings and flag any IQR outliers in the observed_points field across all 90 rows so I can see which days had anomalous data collection?”
Sensor data quality check
“We have up to 150 rows of sensor reading completeness logs with row IDs, dates from 2025, group labels like 'sensor-north' and 'sensor-south', and observed vs expected point counts. Run the IQR outlier detection on expected_points and give me the two analytical reports.”
ETL job monitoring anomaly review
“My ETL monitoring system logged 60 rows of daily completeness records across four pipeline groups — can you run string normalisation and IQR outlier flags on the observed_points field so I can spot which pipeline runs deviated significantly from the norm?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a low-cost ($0.01 USDC), self-contained, local analytical engine to simultaneously normalise string fields and detect IQR-based outliers in time series completeness data — no external data sources, live lookups, or ML predictions involved. Ideal for batch data quality checks on up to 200 rows of observed vs expected point counts across grouped time series.
Side effects
- Input exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- All values identical — IQR is zero, outlier detection may be uninformative
- Null values in numeric fields may affect IQR computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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