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Time Series Completeness — String Normalisation + Base-100 Series
x402
Time Series Completeness — String Normalisation + Base-100 Series 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 string-normalised and base-100 indexed analytics for time series completeness data (observed vs expected points) across up to 200 rows, producing two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Base-100 series: String normalisation + Base-100 series 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 for the supplied rows: (1) a string-normalised representation of the selected time series field across all rows, and (2) a base-100 indexed series anchored at the specified base_index row, enabling trend and completeness comparison across dataset groups and dates.
Example use cases
Daily pipeline completeness benchmarking
“I have daily completeness records for two sensor pipelines from last week — observed vs expected readings — can you string-normalise those values and produce a base-100 series starting from day 0 so I can see how each pipeline drifted relative to its baseline?”
Multi-group data gap analysis
“We have 120 rows of completeness data across 5 dataset groups spanning three months, each with observed_points and expected_points per date. Can you run the string normalisation and base-100 index report on the expected_points field with the base at row index 0?”
QA report for ingestion monitoring
“Our data ingestion monitor logged observed vs expected point counts for every day in Q3 — about 90 rows per group. Can you produce both the string-normalised and base-100 indexed completeness reports so our QA team can see where we fell short?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-030?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of time series completeness records (observed vs expected counts) and need both a string-normalised view and a base-100 indexed series in a single low-cost call ($0.01 USDC). It is a self-contained local analytical engine with no external lookups, predictions, or side effects — ideal for deterministic, reproducible data quality reporting on structured completeness data.
Side effects
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- base_index out of range (0–199) or exceeding submitted row count causes errors
- Null numeric values for both observed_points and expected_points may produce incomplete analytics
- Payment not received or insufficient USDC balance blocks execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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