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Time Series Completeness – Secondary-Measure Weighted Mean + Missing-Value Fill
x402
Time Series Completeness – Secondary-Measure Weighted 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 2026-10-28).
Computes a secondary-measure weighted mean and fills explicit missing values for up to 200 time series completeness records (observed_points, expected_points) using local analytical engines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + 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 secondary-measure weighted mean computed over the specified field (observed_points or expected_points) across all supplied rows, and (2) explicit missing-value fills for null entries in that field using the chosen method (zero, mean, or median). Reports are produced locally without any external lookup or live data.
Example use cases
Weekly pipeline completeness audit
“I have 30 days of sensor pipeline logs with observed_points and expected_points per dataset group — some days have nulls in observed_points. Can you fill the missing values using the mean method and compute the secondary-measure weighted mean so I can see which groups are underperforming?”
Multi-group data quality batch check
“Here are 150 daily completeness records across 5 dataset groups — can you run the weighted mean + missing-value fill analysis on expected_points using zero fill and give me the two analytical reports?”
Null-heavy time series cleanup
“My completeness dataset for October has a lot of nulls scattered across observed_points — can you fill those with the median and calculate the secondary-measure weighted mean across all rows so I can assess overall data coverage?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local, purely analytical computation of weighted means and missing-value imputation for time series completeness data (observed vs expected point counts) without requiring any external data lookup, prediction, or live enrichment. Ideal for batch audits of up to 200 rows where you want two distinct reports in one call at minimal cost ($0.01 USDC).
Side effects
- More than 200 rows submitted — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- All values null in the target field may produce undefined weighted mean
- Invalid enum for method or field causes schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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