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Time Series Completeness — String Normalisation + Trailing Mean Series
x402
Time Series Completeness — String Normalisation + 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).
Runs two analytical reports on time series completeness data: string normalisation of dataset group labels and a trailing mean series for observed or expected data points, across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Trailing mean series: String normalisation + 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
Two analytical reports: (1) a string normalisation report cleaning and standardising the dataset_group label values across the supplied rows, and (2) a trailing mean series computed over the selected field (observed_points or expected_points) using the specified window size, returned for all submitted rows (up to 200).
Example use cases
Pipeline completeness rolling average audit
“I have daily completeness records for my ETL pipeline — about 120 rows with observed_points and expected_points per date across three dataset groups. Can you run a trailing mean over observed_points with a window of 5, and also normalise the dataset group label strings?”
Data ingestion monitoring with smoothed trends
“We track expected vs observed data points for our ingestion jobs every day. Can you compute a trailing mean series on expected_points with a window of 10 across our 80 completeness rows, and normalise the group names at the same time?”
Sensor data completeness quality check
“I've got 30 days of sensor completeness records — each row has a date, a dataset_group, observed_points, and expected_points. Please run string normalisation on the group labels and give me a trailing mean for observed_points using a window of 3.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-028?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation on dataset group labels and a trailing mean time series computed in a single call over completeness records (observed vs expected data points), without requiring any external data lookup or live prediction. Ideal for offline or batch analytical workflows where you already have the completeness data and want two complementary reports at low cost ($0.01 USDC for up to 200 rows).
Side effects
- Missing required fields (row_id, date, observed_points, expected_points) will cause validation errors
- Date values not matching YYYY-MM-DD pattern will be rejected
- Submitting more than 200 rows exceeds the maxItems limit and will be rejected
- Window size outside the range 2–200 will be rejected
- Null values in the selected field may affect trailing mean calculation accuracy
- Invalid enum value for 'field' parameter (must be 'observed_points' or 'expected_points') will cause an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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