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Time Series Completeness — Grouped Numeric Summary + Missing-Value Fill
x402
Time Series Completeness — Grouped Numeric Summary + 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 2026-10-15).
Computes grouped numeric summaries and fills explicit missing values for time series completeness records (observed_points vs expected_points) across up to 200 rows in two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + 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
Returns two independent analytical reports: (1) a grouped numeric summary (e.g. count, mean, min, max, std) of the observed_points and/or expected_points fields segmented by dataset_group, and (2) an explicit missing-value-filled version of the chosen field using the specified fill method (zero, mean, or median). Both reports are based solely on the supplied rows (up to 200).
Example use cases
Daily pipeline completeness audit
“I have 120 rows of daily completeness records for two dataset groups — 'pipeline-a' and 'pipeline-b' — with observed_points and expected_points from 2024-01-01 through 2024-04-30. Can you group them and summarize the numeric stats, and fill any missing observed_points using median?”
Missing data imputation for sensor feed
“My sensor dataset has about 80 rows spanning 2025-03-01 to 2025-05-31, split across three groups, and some expected_points values are null. Please fill those nulls with zeros and give me grouped summary statistics alongside the filled series.”
Completeness gap analysis across experiment groups
“I've got completeness records for two experiment groups over 60 days — each row has a date, group name, observed and expected data points. Run grouped summaries and fill any missing observed_points using mean fill so I can see which group has larger gaps.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-099?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need quick grouped statistical summaries and missing-value imputation on time series completeness records (observed vs expected data points) without any external data lookup, live API calls, or predictions. It is ideal for batch data quality audits where you supply the rows directly and want two reports (summary + filled series) in a single call for a fixed low cost. Prefer alternatives if you need forecasting, anomaly detection, or more than 200 rows.
Side effects
- Input array is empty or exceeds 200 rows — validation error
- Missing required fields (row_id, date, observed_points, expected_points) on any row — schema error
- Date strings not in YYYY-MM-DD format — pattern validation failure
- Field or method enum value not recognized — enum validation error
- All values for the target field are null — fill may produce degenerate results
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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