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Time Series Completeness — String Normalisation + Explicit Missing-Value Fill
x402
Time Series Completeness — String Normalisation + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Normalises string fields and fills explicit missing values (zero, mean, or median) in time series completeness records containing observed and expected data points, processing up to 200 rows with 2 independent reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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 independent analytical reports covering string-normalised and missing-value-filled versions of the submitted time series completeness records, including processed observed_points and expected_points values per row, with no live lookups or external data enrichment.
Example use cases
Imputing gaps in daily sensor coverage data
“I have daily sensor coverage records for two dataset groups with dates from 2026-10-01 through 2026-10-31 — some observed_points values are null. Can you normalise the string fields and fill missing observed_points using median fill, and give me the 2 completeness reports?”
Cleaning expected points for pipeline monitoring
“My pipeline monitoring data has about 80 rows tracking expected_points per day across multiple groups, but some expected_points are missing. Please normalise the dataset_group strings and fill the missing expected_points using mean imputation, then return both analytical reports.”
Zero-filling missing observations for a data audit
“I'm auditing time series completeness for a dataset with 120 rows — some observed_points entries are null. Can you normalise the string columns and zero-fill those missing observed_points values, generating the 2 standard reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-037?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need offline, local analytical processing of time series completeness records (observed vs expected data points) that require both string field normalisation and explicit missing value imputation in a single call. It is ideal for small-to-medium batch jobs (up to 200 rows) where you want two independent reports and no external lookups or predictions. Prefer it over generic data-cleaning services when your data specifically tracks observed_points and expected_points across date-keyed, group-labelled rows.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Invalid date format (not YYYY-MM-DD) — row fails validation
- Missing required fields (row_id, date, observed_points, expected_points) — row rejected
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Empty rows array — minItems:1 constraint violated
- Invalid method enum value — must be zero, mean, or median
- Invalid field enum value — must be observed_points or expected_points
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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