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Time Series Completeness: Trailing Mean + Missing-Value Fill
x402
Time Series Completeness: Trailing Mean + 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 a trailing mean series and fills explicit missing values for time series completeness records (observed vs expected points) for up to 200 rows in two combined reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Trailing mean series + Explicit missing-value fill: Trailing mean series + 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 analytical reports: (1) a trailing mean series over the specified window for the chosen field (observed_points or expected_points), and (2) an explicit missing-value fill report where nulls are replaced using the chosen method (zero, mean, or median) — all computed locally against the supplied rows, with no external data lookup.
Example use cases
Smoothing noisy pipeline completeness metrics
“I have 30 days of completeness records for my data pipeline — observed_points and expected_points per day across two groups. Can you compute a trailing mean with a window of 5 on observed_points and also fill in any missing values using the mean method? I need both reports.”
Detecting and patching gaps in sensor data coverage
“My sensor dataset has 90 rows of daily expected vs observed point counts and some days have nulls in expected_points. Run a window-7 trailing mean and fill those nulls with zero, and give me the two combined analytic reports.”
Monitoring dataset completeness trends over time
“I'm tracking dataset completeness for group-a and group-b over the last 60 days — here are my observed_points and expected_points per day. Can you smooth observed_points with a trailing mean using window size 10 and fill any missing values with median, and return both the trend series and the fill report?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a trailing mean smoothing series and a missing-value imputation report computed together in a single call for time series completeness data (observed vs expected points). It is ideal for lightweight, local batch analytics on up to 200 rows without any external lookups, predictions, or professional assessments — and the $0.01 flat fee covers both reports simultaneously.
Side effects
- Fewer than 1 or more than 200 rows submitted — validation error
- Invalid date format (not YYYY-MM-DD) — schema rejection
- Window size smaller than 2 or larger than 200 — validation error
- observed_points or expected_points values outside ±1,000,000,000,000 — validation error
- Missing required fields (row_id, date, observed_points, expected_points) — schema error
- Invalid method enum value (not zero/mean/median) — schema rejection
- Invalid field enum value (not observed_points/expected_points) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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