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Time Series Completeness: Numeric Interval Filter + IQR Outlier Flags
x402
Time Series Completeness: Numeric Interval Filter + 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) by filtering rows within a numeric interval and flagging IQR-based outliers across up to 200 rows in two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric interval filter + IQR outlier flags: Numeric interval filter + 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 rows that fall within the specified numeric interval for the chosen field (observed_points or expected_points), and one flagging rows that are IQR-based statistical outliers. Each report operates on the same supplied dataset of up to 200 time series completeness rows without any external data lookups.
Example use cases
Data pipeline completeness audit
“I'm monitoring two data pipelines daily and I have 90 days of records with observed_points and expected_points per group. Filter observed_points between 0 and 500, flag any IQR outliers, and give me the two-report analysis so I can see which days had abnormal completeness.”
Sensor reading gap detection
“I've got 150 rows of sensor time series data with observed and expected measurement counts per day per sensor group. Run the numeric interval filter on observed_points from 10 to 1000 and flag any IQR-based outliers so I can identify which sensors had unusual gaps.”
ETL job monitoring anomaly check
“Our ETL jobs log expected vs observed record counts each day across several groups. I have about 80 rows covering the last quarter — can you filter expected_points between 50 and 5000 and flag IQR outliers to spot which jobs were statistically anomalous?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-130?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured time series completeness data (observed vs expected point counts) and need both numeric range filtering and IQR-based outlier detection in a single call, without requiring any external data sources, predictions, or live lookups. It is ideal for data quality monitoring workflows where you want two complementary statistical views — interval membership and distributional anomalies — on the same dataset for up to 200 rows at minimal cost.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not in YYYY-MM-DD format — schema pattern mismatch
- Minimum value greater than maximum value — logically invalid interval producing empty filter results
- Null numeric values in the target field — rows may be excluded from interval filtering
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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