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Time Series Completeness: Base-100 Series + Two-Measure Correlation
x402
Time Series Completeness: Base-100 Series + Two-Measure Correlation 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 indexed series and a two-measure correlation between observed_points and expected_points for up to 200 time series completeness records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Base-100 series + Two-measure correlation: Base-100 series + Two-measure correlation 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 indexed time series rebased from the specified row index for the chosen field (observed_points or expected_points), and (2) a two-measure correlation analysis between observed_points and expected_points across all supplied rows. No external data is fetched; all computation is performed locally on the supplied data.
Example use cases
Data pipeline completeness trending
“I'm tracking daily data ingestion completeness — I have up to 50 rows each with a date, a dataset group name, how many data points we observed, and how many we expected. Can you generate a base-100 indexed series for observed_points starting at index 0 and also give me the two-measure correlation between observed and expected?”
Multi-group completeness comparison
“I have completeness records across two dataset groups — group-a and group-b — over the last 30 days, each with observed and expected data point counts. Please rebase the expected_points series to a base-100 index and run the correlation analysis between observed and expected for all rows.”
QA audit of sensor data coverage
“We're auditing sensor data coverage for October — I have 120 rows with daily observed vs expected point counts per sensor group. Can you normalize the observed_points to a base-100 index starting at row 0 and compute the two-measure correlation between observed_points and expected_points across all rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-165?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a pre-collected batch of time series completeness records (observed vs expected data points) and need both a normalized base-100 index series and a correlation measure in a single low-cost call. It is ideal for offline or periodic analytics workflows where data is already assembled and no live lookup or prediction is needed. Prefer it over general-purpose statistics APIs when you specifically need the completeness-framed dual-report output at minimal cost ($0.01 for both reports).
Side effects
- Exceeding 200 rows causes validation failure
- Missing required fields (row_id, date, observed_points, expected_points) causes rejection
- Invalid date format (non-YYYY-MM-DD) causes schema validation error
- base_index out of range (0–199) causes error
- Null values in both observed and expected may degrade correlation reliability
- Fewer than 2 rows may produce degenerate correlation results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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