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Time Series Completeness — Daily Sum + Secondary-Measure Weighted Mean
x402
Time Series Completeness — Daily Sum + Secondary-Measure Weighted Mean 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 daily sum and secondary-measure weighted mean analytics for time series completeness records (observed vs expected data points) across up to 200 rows in two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Daily sum series + Secondary-measure weighted mean: Daily sum series + Secondary-measure weighted mean 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 daily sum series aggregating the selected measure (observed_points or expected_points) across all rows by date, and (2) a secondary-measure weighted mean that weights the primary field values by the companion measure — all computed locally with no external lookups. Up to 200 input rows supported per call.
Example use cases
Data pipeline daily gap monitoring
“I have 30 days of completeness records for two pipelines — group-alpha and group-beta — each with observed and expected data points. Can you compute the daily sum series and weighted mean using observed_points so I can see where my pipelines are falling short?”
Sensor coverage completeness check
“Here are 90 rows of sensor reading completeness data by date and sensor group, with expected_points and observed_points filled in. Run the daily sum series and secondary-measure weighted mean analytics on this using expected_points as the field so I can assess coverage rates.”
Data quality report for multiple dataset groups
“I'm auditing data completeness for four dataset groups across the past quarter — I have one row per date per group with observed and expected point counts. Give me the daily sum aggregation and the weighted mean report using observed_points to summarize where completeness is highest and lowest.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-142?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary completeness analytics — a daily sum series and a weighted mean — computed together in a single cost-efficient call ($0.01 USDC for both reports). Ideal for data pipeline monitoring, sensor coverage audits, or any scenario where you track observed vs expected data point counts over time across multiple groups. Prefer this over single-report endpoints when you need both aggregation types simultaneously.
Side effects
- Fewer than 1 or more than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error
- Date strings not matching YYYY-MM-DD pattern — schema validation failure
- Null values in both observed_points and expected_points making weighted mean undefined
- Invalid field enum value (must be 'observed_points' or 'expected_points')
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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