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Time Series Completeness — Adjacent Row Changes + Missing-Value Fill
x402
Time Series Completeness — Adjacent Row Changes + 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).
Analyzes time series completeness data by computing adjacent row differences and filling missing values (zero, mean, or median) for observed_points or expected_points fields across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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
Returns two analytical reports: (1) adjacent row changes showing differences between consecutive observed_points or expected_points values per group, and (2) a missing-value fill report where nulls in the selected field are replaced using zero, mean, or median — all computed locally from the supplied rows, with no external lookups.
Example use cases
Daily data pipeline completeness audit
“I have 90 rows of daily completeness records with dates, dataset groups, observed_points, and expected_points — can you compute the adjacent row differences and fill any missing observed_points using the mean so I can see where my pipeline dropped data?”
Sensor coverage gap detection
“Here are 120 rows of sensor coverage data by date and group. I want to see how observed_points changed between consecutive days and fill any nulls in expected_points using zero so I can flag completeness gaps.”
Weekly reporting on data ingestion rates
“I've got 40 rows of weekly ingestion completeness records across several dataset groups. Can you run both the adjacent change analysis and a median fill on the observed_points field so I can include this in my data quality report?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-162?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 time series completeness records and need both adjacent-row change detection and missing-value imputation in a single call. It is best for offline, self-contained analysis of completeness metrics (observed vs expected points) where no external data, predictions, or live lookups are needed. Prefer it over general analytics platforms when you want a lightweight, pay-per-use computation without setting up infrastructure.
Side effects
- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Both observed_points and expected_points are null for all rows, making fill and change computation meaningless
- Payment of 0.01 USDC not completed — endpoint returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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