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Time Series Completeness: Adjacent Row Changes + IQR Outlier Flags
x402
Time Series Completeness: Adjacent Row Changes + 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) for adjacent row-to-row changes and IQR-based outlier detection, returning two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Adjacent row changes + IQR outlier flags: Adjacent row changes + 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 for the supplied rows: (1) adjacent row change analysis showing period-over-period deltas in the selected field (observed_points or expected_points), and (2) IQR-based outlier flags identifying rows whose values fall outside the interquartile range bounds. No external lookups or live data — analysis is performed purely on the submitted rows.
Example use cases
Daily pipeline completeness audit
“I have 60 days of completeness logs for my data pipeline — each row has a date, observed_points, and expected_points. Can you check them for sudden adjacent-row jumps and IQR outliers in the observed_points field so I can see which days had anomalous collection rates?”
Multi-group sensor data gap detection
“My IoT sensor network has two groups reporting daily. I want to analyze 90 rows of completeness data — row IDs, dates, dataset groups, and both observed and expected point counts — looking for IQR outliers in expected_points across the whole series.”
Data quality SLA monitoring
“We track daily completeness for our SLA reporting. Can you take these 45 rows with observed_points and expected_points across three dataset groups and flag both adjacent-row swings and statistical outliers in observed_points so I know where we missed targets?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-157?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of time series completeness data (observed vs expected point counts) and need both adjacent row change detection and IQR-based outlier flagging in a single low-cost call. It is ideal for data quality monitoring pipelines, SLA audits, or anomaly triage where no external data or predictions are needed — just statistical analysis on your submitted records. Prefer this over general analytics tools when you need two complementary reports (trend changes + statistical outliers) on completeness metrics specifically.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Null values in numeric fields may affect IQR computation accuracy
- Payment of 0.01 USDC not completed — 402 payment required response
- Invalid field selector (not 'observed_points' or 'expected_points') — enum validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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