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Time Series Completeness — Two-Measure Correlation + Explicit Missing-Value Fill
x402
Time Series Completeness — Two-Measure Correlation + Explicit Missing-Value Fill 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 Pearson correlation between observed_points and expected_points in a time series and fills missing values using zero, mean, or median imputation — delivered as two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill 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 Pearson correlation report showing the statistical relationship between observed_points and expected_points across the supplied rows, and (2) a missing-value-filled version of the chosen field (observed_points or expected_points) using the selected imputation method (zero, mean, or median). No predictions, external lookups, or live data — purely local computation on the supplied rows.
Example use cases
IoT sensor gap analysis
“I have 120 rows of daily IoT sensor readings with observed_points and expected_points per device group from 2025-01-01 to 2025-04-30. Some observed_points are null — can you fill those with the median and then run a correlation between observed and expected points so I can see how complete my sensor data really is?”
Financial data completeness audit
“Our finance team tracks expected vs observed transaction counts per date and dataset_group across 80 rows. Fill any missing expected_points with zero and compute the correlation between observed_points and expected_points — I need both analytical reports to present at our data quality review.”
Marketing campaign delivery check
“I've got 60 rows of campaign delivery data with observed_points (actual impressions) and expected_points (planned impressions) by date and channel group. Can you impute missing observed_points using the mean and show me how strongly the observed data correlates with what we planned?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-184?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 rows of time series completeness records (observed vs expected point counts) and need both a correlation analysis and missing-value imputation in a single call at low cost ($0.01 USDC). It is ideal for offline/batch data quality checks where no live data lookup or prediction is needed. Prefer alternatives if you need outlier flagging (see sibling IQR endpoint), grouped summaries, or more than 200 rows.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds batch limit
- Invalid date format (not YYYY-MM-DD) — validation error
- Both observed_points and expected_points are null for all rows — correlation undefined
- Invalid method enum value — schema validation error
- Invalid field enum value — schema validation error
- Payment not included or incorrect amount — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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