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Feature Availability Trailing Mean Series and Missing Value Fill
x402
Feature Availability Trailing Mean Series and 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 2026-10-16).
Computes a trailing moving average series and fills explicit missing values for feature availability records (populated_features / expected_features) over up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied feature availability records (populated_features, expected_features). 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 showing the rolling average over the specified window for the chosen field, and (2) an explicit missing-value fill report replacing nulls using the selected method (zero, mean, or median). Both reports cover up to 200 input rows and are computed locally without external lookups.
Example use cases
ML pipeline feature completeness smoothing
“I have 120 daily records from my ML feature store with populated_features and expected_features counts per dataset group. Can you run a trailing mean with a window of 14 on populated_features and fill any nulls with zero? Here's the data.”
Data quality gap analysis by group
“My data team tracks how many features are populated versus expected each day across two dataset groups. Run a 5-day trailing mean on expected_features with median fill for missing values on these 80 rows so I can see the smoothed trend.”
Feature availability reporting for dashboards
“I need a smoothed feature availability report for our weekly review — use a trailing mean window of 3 and mean imputation for any gaps in populated_features across these 30 date-stamped records split by dataset group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a rolling/trailing mean time series and missing-value imputation on feature availability records in a single low-cost ($0.01) call. Ideal for ML feature stores, data quality pipelines, or analytics dashboards where you supply your own data (up to 200 rows) and want offline analytical computation without any external data lookups or live API calls.
Side effects
- Fewer than 1 or more than 200 rows supplied — returns validation error
- Invalid date format (not YYYY-MM-DD) — rejected at schema validation
- Window size less than 2 or greater than 200 — rejected at schema validation
- populated_features or expected_features values outside ±1,000,000,000,000 range — schema error
- Missing required fields (row_id, date, dataset_group) — schema validation failure
- All values null for selected field — imputation may produce degenerate output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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