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Time Series Completeness — Date by Category Pivot + Keyed Dataset Comparison
x402
Time Series Completeness — Date by Category Pivot + 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 2026-10-19).
Analyzes time series completeness records by producing a date-by-category pivot table and a keyed dataset comparison report from supplied observed/expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Date by category pivot + Keyed dataset comparison: Date by category pivot + 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 analytical reports: (1) a date-by-category pivot table showing the selected field (observed_points or expected_points) broken down by date and dataset_group, and (2) a keyed dataset comparison between the primary rows and the secondary 'other' dataset, highlighting differences in completeness metrics. Up to 200 rows processed per call.
Example use cases
IoT Sensor Coverage Audit
“I have two months of IoT sensor readings organized by sensor group and date — each row has observed_points and expected_points. Can you pivot the data by date and group and then compare it against last month's baseline dataset to see where completeness dropped?”
Multi-Group Data Pipeline QA
“We're running a QA check on our ETL pipeline. I've got a primary dataset and a reference dataset, both with observed and expected data point counts by date and dataset_group. Can you generate a date-by-category pivot on observed_points and then do a keyed comparison between the two to flag discrepancies?”
Marketing Campaign Metrics Completeness Check
“I need to audit the completeness of our daily campaign metrics — I have records for two campaign groups over the past week with observed and expected data point counts. Please pivot those by date and category, and compare the current week's dataset against last week's to see where we're missing data.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-188?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously produce a date-by-category pivot AND compare two versions or snapshots of a time series completeness dataset in a single paid call. It is ideal for data quality audits, ETL validation, and completeness monitoring where you have structured rows with observed vs. expected point counts grouped by category and date. Prefer this over single-report endpoints when you need both pivot and comparison analytics at once to minimize cost.
Side effects
- Empty rows array — request rejected (minItems: 1)
- Rows exceed 200 — request rejected (maxItems: 200)
- Invalid date format (not YYYY-MM-DD) — validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — schema validation failure
- Invalid field enum value (not 'observed_points' or 'expected_points') — error
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Payment failure — x402 payment not processed, request blocked
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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