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Time Series Completeness — Base-100 Series + Explicit Missing-Value Fill
x402
Time Series Completeness — Base-100 Series + 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 a base-100 indexed series and fills missing values (zero, mean, or median) for time series completeness data containing observed vs. expected point counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Base-100 series + Explicit missing-value fill: Base-100 series + 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 analytical reports: (1) a base-100 indexed series where values are normalized relative to the specified base row, and (2) an explicit missing-value filled series using the chosen imputation method (zero, mean, or median) — both applied to the selected field (observed_points or expected_points) across up to 200 input rows with row_id, dataset_group, date, observed_points, and expected_points.
Example use cases
Monitoring pipeline data completeness over time
“I'm tracking whether our data pipelines are collecting the right number of points each day. I have 30 rows of daily records with observed_points and expected_points for two pipelines, group-alpha and group-beta. Can you index the observed_points to base-100 starting at row 0 and fill any missing values using the mean, so I can see relative completeness trends?”
Normalizing sensor data for cross-site comparison
“I have sensor completeness records from two sites across 60 days — each row has a date, site name, observed_points, and expected_points. Please produce a base-100 series for expected_points anchored at row index 0 and fill any gaps with zero so I can compare both sites on a level playing field.”
Auditing historical data gaps in a batch import
“We're auditing a historical import of 120 daily records across dataset groups 'ingest-v1' and 'ingest-v2'. Can you generate a base-100 indexed series starting at row 0 and use median fill for any missing observed_points values, so we can see where collection fell short relative to expectations?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a base-100 normalized time series view AND missing-value imputation on completeness data (observed vs. expected point counts) in a single $0.01 call covering up to 200 rows. It is ideal for data quality monitoring, pipeline audits, and cross-group completeness comparisons where you want relative indexing and gap filling without any external data lookups or predictions.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- base_index out of range (0–199) causes an error
- null values in both observed_points and expected_points may produce degenerate fill results
- Payment not included or insufficient USDC — x402 payment required before response is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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