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Dataset Partition Sizes — Trailing Mean Series + IQR Outlier Flags
x402
Dataset Partition Sizes — Trailing Mean Series + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes trailing moving averages and IQR-based outlier flags for dataset partition size metrics (row_count, file_mb) across up to 200 supplied records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset partition sizes — Trailing mean series + IQR outlier flags: Trailing mean series + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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 covering: (1) a trailing mean time series for the selected metric (row_count or file_mb) across the supplied records using the specified window size, and (2) IQR-based outlier flags identifying which records deviate significantly from the interquartile range. Results are computed locally with no external lookups.
Example use cases
Pipeline partition spike detection
“I have 120 daily dataset partition records with row counts and file sizes — can you compute a 14-day trailing mean and flag any IQR outliers in the row_count field so I can see which days had abnormal partition sizes?”
File size trend monitoring across groups
“Here are 80 partition size records split across three dataset groups. Run a trailing mean with a window of 5 and flag file_mb outliers using IQR so I can identify which groups have erratic file sizes.”
Data quality audit before ETL promotion
“Before I promote this dataset to production, analyze these 200 partition records — compute a trailing mean series and mark any row_count values that fall outside the IQR range as outliers, using a window of 10.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-07-149?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 dataset partition size records and need both a trailing moving average series and IQR-based outlier detection in a single call without any external data lookups. It is well-suited for data engineers auditing ETL pipeline health, detecting partition size spikes, or performing pre-promotion quality checks on batch dataset records. Prefer this over generic statistics APIs when you need the combined trailing-mean + IQR outlier report format specifically for row_count or file_mb metrics.
Side effects
- Fewer than 1 row submitted — minimum 1 row required
- More than 200 rows submitted — payload exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Window size larger than number of rows may produce sparse or no trailing means
- null values in row_count or file_mb may reduce effective sample for IQR computation
- Missing required fields (row_id, dataset_group, date) cause request rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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