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Time Series Completeness — Base-100 Series + Secondary-Measure Weighted Mean
x402
Time Series Completeness — Base-100 Series + 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 a Base-100 index series and a secondary-measure weighted mean for time series completeness records (observed vs expected data points), returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Base-100 series + Secondary-measure weighted mean: Base-100 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 analytical reports: (1) a Base-100 index series normalizing each row's field value relative to the chosen base index row, and (2) a secondary-measure weighted mean computed across the supplied completeness records (observed_points and expected_points). Both reports are computed locally on the supplied data with no external lookups.
Example use cases
Daily pipeline completeness monitoring
“I have 30 days of pipeline completeness data with observed and expected data points per day across two dataset groups. Can you index them to a Base-100 series starting from day 0 and also compute the weighted mean so I can see where coverage dipped?”
Multi-group data gap analysis
“Here are 90 rows of completeness records for three dataset groups — each with a date, observed_points, and expected_points. Run the Base-100 index series and weighted mean analysis on the expected_points field, anchoring to base index 0.”
Monthly reporting on data feed completeness
“I need two analytics reports for our monthly data feed review: a Base-100 normalized series and a secondary weighted mean, based on observed vs expected points for up to 150 rows of records split across four groups. Can you compute those?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-166?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected time series completeness data (observed vs expected point counts) and need two specific statistical outputs — a Base-100 normalized index series and a weighted mean — computed offline against your supplied rows. Ideal for data quality monitoring, pipeline completeness reporting, or audit scenarios where you own the data and need fast, deterministic analytics without external API calls or live data lookups. Not suitable for live data fetching, forecasting, or professional data assessments.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema validation error
- base_index value out of range (0–199) returns an error
- Null values in observed_points or expected_points may affect weighted mean accuracy
- Submitting fewer than 1 row returns an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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