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Aggregation Checks — Trailing Mean Series + Keyed Dataset Comparison
x402
Aggregation Checks — Trailing Mean Series + 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 2026-10-24).
Runs two analytical reports on user-supplied aggregation check records: a trailing mean time series and a keyed dataset comparison, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Aggregation checks — Trailing mean series + Keyed dataset comparison: Trailing mean series + Keyed dataset comparison for supplied aggregation checks records (source_sum, aggregate_sum). 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 trailing mean time series computed over the specified field (source_sum or aggregate_sum) across the supplied rows, and (2) a keyed dataset comparison between the primary rows and the secondary 'other' dataset, highlighting differences by row_id and dataset_group. Results are computed locally with no external lookups.
Example use cases
Financial reconciliation drift detection
“I have two versions of our monthly revenue rollup — the source ledger rows and the aggregated totals by region — each with row IDs, dates, and source_sum and aggregate_sum values. Can you run a trailing mean series plus a keyed comparison across both datasets to see where the numbers are drifting apart?”
ETL pipeline aggregation audit
“Our ETL pipeline produces daily source_sum and aggregate_sum figures by dataset_group for up to 150 rows. Can you compute a trailing mean over time and do a keyed dataset comparison between this week's run and last week's run to catch any unexpected changes?”
Multi-group sum consistency check
“I have two batches of aggregation check records — both broken into group-a and group-b with dated rows — and I want to validate consistency using a trailing mean series on the aggregate_sum field and a side-by-side keyed comparison of the two batches.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-17-153?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a trailing mean time series analysis AND a keyed cross-dataset comparison in a single call on aggregation check records (source_sum vs aggregate_sum). It is ideal for reconciliation workflows, ETL auditing, and financial data quality checks where you already have your data structured with row_id, date, dataset_group, source_sum, and aggregate_sum fields and want two complementary statistical reports at minimal cost.
Side effects
- Fewer than 1 row supplied — request rejected by minItems constraint
- More than 200 rows submitted — truncation or rejection due to maxItems limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- source_sum or aggregate_sum values outside ±1,000,000,000,000 are rejected
- Missing required fields (row_id, dataset_group, date) cause row-level errors
- Mismatched row_ids between primary and 'other' dataset reduce comparison coverage
- Payment of 0.01 USDC not completed — request blocked by x402 paywall
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array | |
| window | integer |
Tags
x402
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