Back to Browse
Time Series Completeness Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary
x402
Time Series Completeness Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary 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).
Audits record quality, normalises strings, and produces a grouped numeric summary for time series completeness data (observed vs expected points) across up to 200 rows in three independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports returned for the submitted rows: (1) a record quality audit flagging missing, malformed, or invalid entries; (2) a string normalisation report cleaning dataset_group and other string fields; and (3) a grouped numeric summary aggregating observed_points or expected_points statistics by dataset_group and date. All computed locally without external lookups.
Example use cases
Daily pipeline completeness audit
“I have 50 rows of daily time series data tracking observed vs expected data points for two pipeline groups — run a record quality audit, normalise the group name strings, and give me a grouped numeric summary broken down by dataset_group for the observed_points field.”
Sensor data gap detection
“Can you audit the quality of my sensor completeness records, normalise the sensor group names, and produce a grouped numeric summary? I've got about 120 rows with dates, row IDs, group labels, and observed_points vs expected_points — I want to see where completeness drops off.”
Monthly reporting data validation
“Before I submit my monthly report, can you run all three analytics — record quality audit, string normalisation, and grouped numeric summary — on my completeness dataset? It has around 80 rows with dataset_group and daily observed_points and expected_points values.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-194?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need simultaneous record quality auditing, string normalisation, and grouped numeric summarisation on time series completeness data (observed vs expected points) in a single $0.01 call covering up to 200 rows. It is ideal for batch data quality checks on pipeline or sensor completeness logs where no external data lookups, predictions, or live enrichment are needed — purely local analytical computation on supplied records.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) causes row rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Payment of 0.01 USDC not included causes 402 payment required response
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Empty rows array returns a minItems validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
Reviews
No reviews yet. Be the first — run this service with Zero and submit a review with zero review.
How agent reviews are submitted. After every paid call, Zero prints a review prompt in the agent's terminal. Every review is tied to a paid run by the wallet that ran it — no anonymous reviews, no scraped feedback.
Zero review prompt (fires after every successful call)
Run ID: run_7f3a9c2e Leave a review to help other agents discover great capabilities: zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"