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Time Series Completeness: Exact Row Deduplication + Base-100 Series
x402
Time Series Completeness: Exact Row Deduplication + Base-100 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).
Deduplicates rows and generates a base-100 indexed series from time series completeness data (observed vs. expected data points) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Exact row deduplication + Base-100 series: Exact row deduplication + Base-100 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 analytical reports: (1) a deduplicated version of the input rows with exact duplicate entries removed, and (2) a base-100 indexed series for the specified field (observed_points or expected_points) anchored at the given base_index row, enabling normalized trend comparison across dataset groups.
Example use cases
Deduplicating sensor completeness logs
“I have 150 rows of daily completeness data for our IoT sensor pipeline — each row has a row_id, dataset_group, date, observed_points, and expected_points — but I suspect some rows were duplicated during export. Can you deduplicate them and also produce a base-100 series for observed_points starting at index 0?”
Rebasing expected data point series
“Take my time series completeness records for the 'billing' dataset group — about 90 rows from 2024-07-01 to 2024-09-28 — and rebase the expected_points values to 100 starting from the first row, so I can compare trends across groups on a normalized scale.”
Multi-group completeness comparison
“I have completeness records for three dataset groups — 'web', 'mobile', and 'api' — totalling 180 rows with dates, observed_points, and expected_points. Please deduplicate and give me a base-100 series on observed_points anchored at index 0, so I can see how each group's completeness evolved relative to that baseline.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-047?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need offline, local analytical processing of time series completeness data — specifically exact row deduplication combined with base-100 series normalization — without any external lookups, predictions, or live data access. It is well-suited for data quality pipelines where you supply up to 200 rows of observed vs. expected point counts and want two structured reports (deduplication + rebased index) in a single low-cost call.
Side effects
- More than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- base_index out of range for submitted row count — error or unexpected indexing
- Null values in the target field causing incomplete series generation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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