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Time Series Completeness: Equal-Width Histogram + IQR Outlier Flags
x402
Time Series Completeness: Equal-Width Histogram + 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).
Generates an equal-width histogram and IQR-based outlier flags for time series completeness data (observed vs expected points) across up to 200 rows, producing 2 independent analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Equal-width histogram + IQR outlier flags: Equal-width histogram + 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) an equal-width histogram showing the distribution of the selected field (observed_points or expected_points) across the specified number of bins, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range boundaries. No external lookups or predictions are made — all computation is local and deterministic.
Example use cases
Data pipeline completeness audit
“I have daily sensor data from October 2026 across two groups — group-a and group-b — with observed and expected point counts for each day. Can you run a histogram with 8 bins and flag any IQR outliers in the observed_points field so I can see which days had unusual data volumes?”
Detecting missing data anomalies
“My ETL job logs expected_points vs observed_points for each dataset per day. I want to find outlier days where observed_points were way off. Run IQR outlier detection and a 5-bin histogram on these 120 rows for me.”
Monitoring dataset group completeness
“I want to compare how complete our data collection was across different dataset groups last month. Here are 80 rows with row_id, date, dataset_group, observed_points, and expected_points — please run the equal-width histogram with 10 bins and IQR outlier flagging on expected_points.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-170?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local statistical analysis of time series completeness records — specifically equal-width histogram distribution and IQR outlier detection — without requiring any external data lookup, live prediction, or human assessment. Ideal for data quality pipelines where you want to detect anomalous days or groups in observed vs expected point counts across up to 200 rows, at low cost ($0.01 USDC for 2 reports).
Side effects
- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Missing required fields (row_id, date, observed_points, or expected_points) in any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Bins value outside 1–30 range — rejected by schema
- All values are null for the selected field — insufficient data for IQR computation
- Invalid field enum value — only 'observed_points' or 'expected_points' accepted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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