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Time Series Completeness — String Normalisation + Secondary-Measure Weighted Mean
x402
Time Series Completeness — String Normalisation + 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).
Analyzes up to 200 time series completeness records by performing string normalisation and secondary-measure weighted mean calculations across observed vs expected data points, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Secondary-measure weighted mean: String normalisation + 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
Two independent analytical reports: (1) a string normalisation report applying normalisation logic to the dataset group labels and row identifiers in the supplied records, and (2) a secondary-measure weighted mean report computing a weighted average over the selected numeric field (observed_points or expected_points) across the submitted time series completeness rows. Both reports are generated from the supplied data using local analytical engines with no external lookups.
Example use cases
Daily pipeline completeness audit
“I have 150 rows of daily completeness data for my ingestion pipelines across two groups, with observed and expected point counts from last month — can you run the string normalisation plus secondary-measure weighted mean analysis on the observed_points field and return both reports?”
Dataset group weighted mean comparison
“I need to compare how complete my dataset groups were over Q3 — I've got rows with date, dataset_group, observed_points, and expected_points. Can you compute the string normalised and weighted mean summaries across all 80 rows using the expected_points field?”
Monitoring completeness anomalies across dates
“Can you analyze these 60 completeness records — each has a row_id, group label, date, and both observed and expected point counts — using string normalisation and secondary-measure weighted mean on observed_points, so I can spot where data collection fell short?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary analytical perspectives — string normalisation and weighted mean — on time series completeness records (observed vs expected points) in a single low-cost call. It is well suited for data quality workflows where you want to process up to 200 dated, grouped completeness rows without triggering external lookups or live data retrieval. Prefer it over general-purpose analytics when your data is already structured with row_id, dataset_group, date, observed_points, and expected_points fields.
Side effects
- Fewer than 1 row or more than 200 rows submitted — rejected due to minItems/maxItems constraint
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside allowed range (-1e12 to 1e12) — validation error
- Payment not completed — endpoint returns 402 Payment Required
- Null values in both observed_points and expected_points may produce degenerate weighted mean output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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