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Time Series Completeness — Daily Sum Series + Keyed Dataset Comparison
x402
Time Series Completeness — Daily Sum Series + Keyed Dataset Comparison 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 records by computing a daily sum series and comparing two keyed datasets across up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Daily sum series + Keyed dataset comparison: Daily sum series + Keyed dataset comparison 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: (1) a daily sum series aggregating the selected field (observed_points or expected_points) by date across all rows, and (2) a keyed dataset comparison showing differences between the two supplied datasets (rows and other) matched by row_id/dataset_group/date. Both reports are computed locally from the supplied data with no external lookups.
Example use cases
Pipeline data gap audit
“I'm monitoring a data ingestion pipeline and have daily completeness records for two groups — 'group-a' and 'group-b' — each row has an observed_points and expected_points count per date. Can you compute the daily sum series on observed_points and compare the two dataset groups to show me where we're falling short of expectations?”
A/B dataset completeness check
“I have two versions of our sensor time series dataset — a baseline and a recent update — each with rows tagged by row_id, dataset_group, and date, with observed and expected point counts. Please run both the daily sum aggregation and the keyed dataset comparison so I can see which dates and groups have completeness issues.”
Historical completeness trend review
“We collected daily observed_points and expected_points for our event tracking system over the past few months across multiple dataset groups. I want to see a daily sum series on observed_points and a comparison between two of our dataset snapshots to identify any completeness regressions.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-144?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured time series completeness records with observed vs expected point counts and need both a daily aggregation report and a side-by-side keyed comparison between two datasets in a single low-cost call. Ideal for data quality audits, pipeline monitoring, and completeness gap detection on up to 200 rows without needing live data lookups or external integrations.
Side effects
- Input array contains more than 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) on any row
- Date strings not matching YYYY-MM-DD pattern — schema validation error
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Empty rows array (minItems: 1 not satisfied)
- Payment not processed — x402 payment required before response is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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