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Time Series Completeness: Adjacent Row Changes + Keyed Dataset Comparison
x402
Time Series Completeness: Adjacent Row Changes + 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 2026-10-28).
Analyzes time series completeness records by detecting adjacent row changes and comparing two keyed datasets, producing two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Adjacent row changes + Keyed dataset comparison: Adjacent row changes + 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) an adjacent row changes report showing how the selected field (observed_points or expected_points) shifts between consecutive rows, and (2) a keyed dataset comparison report highlighting differences between the primary rows dataset and the supplied comparison dataset, keyed by row_id and dataset_group. All analysis is performed locally on supplied data with no external lookups.
Example use cases
Daily data pipeline completeness audit
“I have my pipeline's completeness log for the last 90 days — each row has a date, row_id, group name, observed_points, and expected_points. Can you run adjacent row change detection on the observed_points field and also compare it against last quarter's dataset using a keyed comparison?”
Cross-version dataset discrepancy check
“I've got two versions of our completeness dataset — version A and version B — both with row_id, date, dataset_group, observed_points, and expected_points. Run a keyed comparison between them and also show me the adjacent row changes in observed_points so I can see what shifted between versions.”
QA validation for sensor data completeness
“Our sensor monitoring system exports daily completeness records with observed vs expected data points per group. Can you analyze adjacent row changes and do a keyed dataset comparison between this week's export and last week's, analyzing the observed_points field across up to 150 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-161?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both adjacent row change detection and a keyed dataset comparison on time series completeness data (observed vs expected points) in a single $0.01 call. Ideal for data quality pipelines that track sensor completeness, ETL pipeline output validation, or version-to-version dataset diffing — all without requiring any external data sources or live lookups. Prefer over single-report endpoints when you want both analyses bundled cost-effectively.
Side effects
- Fewer than 1 row supplied — minItems validation error
- More than 200 rows supplied — maxItems validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — schema validation error
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- row_id exceeds 80 characters — maxLength validation error
- Numeric values outside ±1,000,000,000,000 range — bounds validation error
- Payment not processed or insufficient USDC balance — x402 payment failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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