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Time Series Completeness — Exact Row Deduplication + Adjacent Row Changes
x402
Time Series Completeness — Exact Row Deduplication + Adjacent Row Changes 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 up to 200 time series completeness records to produce two reports: exact duplicate row detection and adjacent-row change detection for observed_points or expected_points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Exact row deduplication + Adjacent row changes: Exact row deduplication + Adjacent row changes 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) an exact row deduplication report identifying rows with identical field values across the supplied records, and (2) an adjacent row change report highlighting where consecutive rows differ in the selected field (observed_points or expected_points). Both reports are computed locally from supplied data with no external lookups.
Example use cases
Audit duplicate completeness records
“I have a time series completeness log with row_id, date, dataset_group, observed_points, and expected_points for the past 30 days — can you run exact row deduplication on it to find any repeated entries and also show me where adjacent rows changed in the observed_points field?”
Day-over-day change tracking for sensor data
“Here's my sensor completeness dataset with observed_points and expected_points across multiple dataset groups — can you detect where consecutive rows show changes in expected_points and also flag any exact duplicate rows?”
Data quality check before pipeline ingestion
“Before I ingest this completeness time series into our pipeline, can you run the deduplication and adjacent-change reports on the observed_points field? I'll provide the rows with row_id, dataset_group, date, observed_points, and expected_points — about 120 rows total.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-046?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need simultaneous exact-duplicate detection and adjacent-row change analysis on a time series completeness dataset of up to 200 rows, at a fixed cost of $0.01 USDC for both reports together. It is ideal for data quality audits, pipeline pre-ingestion checks, or monitoring completeness logs where both deduplication and change detection are needed in one call without any external dependencies or live lookups.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Numeric values outside the allowed range cause schema validation failure
- Empty rows array (minItems:1 not met) results in an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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