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Time Series Completeness Analyzer — String Normalisation & Adjacent Row Changes
x402
Time Series Completeness Analyzer — String Normalisation & 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 2026-10-28).
Analyzes time series completeness records by applying string normalisation and detecting adjacent row changes across observed_points and expected_points fields, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Adjacent row changes: String normalisation + 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
Returns two analytical reports for the submitted rows: (1) a string normalisation report identifying inconsistent or denormalized string values in the dataset_group and related fields, and (2) an adjacent row changes report flagging where consecutive rows show value shifts in the selected field (observed_points or expected_points). Both reports are computed locally without external lookups or live data enrichment.
Example use cases
Audit daily pipeline completeness gaps
“I have daily completeness records for my ETL pipeline — each row has a date, dataset_group, observed_points, and expected_points. Can you normalize the strings and flag where consecutive rows show changes in observed_points so I can spot missing data runs?”
Detect sensor data drop-offs by group
“Here are 150 rows of sensor completeness metrics grouped by sensor_id across dates, with observed and expected point counts. Run string normalisation on the dataset_group field and detect adjacent row changes in expected_points so I can identify where sensors dropped off.”
QA check on reporting completeness records
“Before I publish my weekly completeness report, can you take these 80 rows — each with a row_id, date, dataset_group, observed_points, and expected_points — and give me the string normalisation report plus the adjacent row change report on observed_points?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-029?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 time series completeness rows and need both string normalisation analysis and adjacent row change detection in a single paid call. Ideal for data quality audits on pipeline completeness logs where you want to identify naming inconsistencies in dataset groups and spot consecutive-row anomalies in point counts without making external API calls or requiring live data.
Side effects
- Exceeds 200 row limit — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Null values in observed_points or expected_points may affect adjacent change calculations
- Invalid field enum value (must be 'observed_points' or 'expected_points') — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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