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Time Series Completeness — String Normalisation + Keyed Dataset Comparison
x402
Time Series Completeness — String Normalisation + 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).
Normalises strings and performs keyed dataset comparison on two supplied time series completeness datasets (observed_points vs expected_points) for up to 200 rows each, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Keyed dataset comparison: String normalisation + 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
Two independent analytical reports: (1) a string normalisation report that standardises the text fields across the submitted rows, and (2) a keyed dataset comparison report that identifies matches, mismatches, and discrepancies between the primary and secondary datasets based on row keys, dates, groups, and observed vs expected point counts. No live lookups, predictions, or external actions are performed — analysis is entirely local to the supplied data.
Example use cases
Daily pipeline data completeness audit
“I have two CSV exports from my data pipeline — a primary run and a reference run — both with row IDs, dates, group names, observed_points, and expected_points for October 2026. Can you normalise the strings and compare them as keyed datasets to show me where the observed counts diverge from expected?”
Sensor reading gap detection
“I need to check completeness of sensor readings across two sensor groups over the last week — I've got about 150 rows per dataset with row IDs, dates, group labels, observed_points and expected_points. Can you run a string normalisation and keyed comparison report to flag where observations fall short of expectations?”
A/B dataset completeness comparison
“We ran two versions of a data collection job and I want to compare their completeness. I have two sets of records (up to 200 rows each) with date, row_id, dataset_group, observed_points, and expected_points. Please normalise the strings and do a keyed dataset comparison to highlight differences between the two runs.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-036?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to validate time series completeness by comparing observed data point counts against expected counts across dates and dataset groups, specifically requiring both string normalisation (to clean up group/key labels) and keyed dataset comparison (to match records across two dataset variants) in a single call. Ideal for data quality pipelines, ETL audits, and sensor/event monitoring workflows where you have up to 200 rows and need two complementary analytical angles on the same data without any external lookups or predictions.
Side effects
- Fewer than 1 row submitted — request rejected
- More than 200 rows in either array — request rejected (maxItems limit)
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Date strings not matching YYYY-MM-DD pattern — validation error
- observed_points or expected_points values outside allowed numeric range — validation error
- Payment of 0.01 USDC not fulfilled — 402 response, no report returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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