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Time Series Completeness — Exact Row Deduplication + Trailing Mean Series
x402
Time Series Completeness — Exact Row Deduplication + Trailing Mean Series 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 time series completeness records by performing exact row deduplication and computing a trailing mean series for observed vs. expected data points, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Exact row deduplication + Trailing mean series: Exact row deduplication + Trailing mean series 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 reports: (1) a deduplicated version of the input rows with exact duplicates removed, and (2) a trailing mean series computed over the specified field (observed_points or expected_points) using the given window size — all derived locally without external lookups or predictions.
Example use cases
Data pipeline completeness audit
“I have 80 rows of daily completeness records for two dataset groups — each row has a date, observed_points, and expected_points. Can you deduplicate them and compute a trailing mean with a window of 7 on observed_points so I can spot where data delivery was consistently below expectations?”
Trailing mean smoothing for monitoring dashboard
“Take these 120 time series rows with observed and expected data points across several dataset groups, deduplicate any exact duplicate rows, then compute a trailing mean series over expected_points with a window size of 5 — I want to use this to smooth out my completeness monitoring charts.”
QA check on ingestion completeness logs
“I've got 30 daily completeness log entries per dataset group and I want to find and remove any exact duplicate rows, then calculate a rolling mean with window 2 on observed_points to help QA our ingestion pipeline — can you run both reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-045?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need both exact row deduplication and a trailing mean series computed together on time series completeness data (observed vs. expected points) in a single low-cost call. It is ideal for data quality workflows, pipeline monitoring, and completeness audits where local computation — without external lookups, predictions, or live data — is sufficient. Best when your dataset fits within 200 rows and you want two complementary analytical views returned at once for $0.01 USDC.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) in one or more rows — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size outside allowed range (2–200) — parameter error
- All rows are duplicates — deduplication report returns empty or minimal dataset
- Payment not completed via x402 — endpoint returns 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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