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Time Series Completeness — IQR Outlier Flags + Explicit Missing-Value Fill
x402
Time Series Completeness — IQR Outlier Flags + Explicit Missing-Value Fill 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 points) using IQR-based outlier detection and explicit missing-value filling, returning two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill 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
Two independent analytical reports: (1) IQR outlier flag report identifying which rows have outlying values in the specified field across all rows, and (2) an explicit missing-value fill report where null/missing values in the specified field are replaced using the chosen method (zero, mean, or median). Results are scoped to the supplied rows (up to 200) and computed locally without external lookups.
Example use cases
Daily pipeline completeness audit
“I have 120 daily records from our data pipeline with observed_points and expected_points per dataset group — please flag any IQR outliers in observed_points and fill in any missing values using median so I can see both the outlier report and the filled data.”
Sensor coverage gap detection
“Here are 90 rows of sensor coverage data by date and group, each with expected_points and observed_points. Can you run IQR outlier flagging on expected_points and fill the missing ones with zero, giving me two separate analysis reports?”
ETL completeness QA before reporting
“Before I finalize my weekly report, can you check these 60 completeness records for outliers using IQR and also fill any null observed_points with the mean? I need both the outlier flags and the filled dataset back.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-180?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both IQR-based outlier detection AND missing-value imputation applied to time series completeness data in a single paid call. It is particularly suited for data quality workflows involving observed vs expected point counts grouped by date and category, with up to 200 rows. It is purely local/analytical — no predictions, live data lookups, or external actions are performed. Prefer it over single-report endpoints when you need both outlier flags and filled data simultaneously at low cost ($0.01 for both reports).
Side effects
- Exceeding 200 rows returns an error or truncation
- Invalid date format (not YYYY-MM-DD) causes validation failure
- Missing required fields (row_id, observed_points, expected_points) cause rejection
- Providing all-null values for the target field may produce degenerate fill results
- Non-numeric values in observed_points or expected_points fail schema validation
- Empty rows array is rejected (minItems: 1)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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