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Dataset Storage Analytics — Trailing Mean Series & Missing-Value Fill
x402
Dataset Storage Analytics — Trailing Mean Series & 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 2026-10-15).
Computes trailing moving-average series and fills missing values in dataset storage records (used_mb, row_count) for up to 200 rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied dataset storage records (used_mb, row_count). 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 for the supplied dataset storage rows: (1) a trailing moving-average series computed over the specified window for the chosen field (used_mb or row_count), and (2) the same series with missing values explicitly filled using the chosen method (zero, mean, or median). All computations are performed locally without any external lookups or predictions.
Example use cases
Smoothing storage usage over time
“I have 120 rows of daily dataset storage records with used_mb values for two groups — can you compute a trailing mean with a window of 7 and fill any nulls with zero so I can see smoothed storage trends?”
Imputing missing row counts before reporting
“My row_count data has some missing entries across 80 records. Can you fill those gaps using the median method and also give me a trailing mean series with a window of 5?”
Multi-group storage trend analysis
“I've got storage records for three dataset groups over 60 dates. Please run a trailing mean with window size 4 on used_mb and use mean imputation for any missing values — I need both reports.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both rolling/trailing mean smoothing and missing-value imputation on dataset storage metrics (used_mb or row_count) in a single call, for up to 200 rows, without requiring any external data source or live system integration. It is ideal for preprocessing storage time-series data before visualization or downstream analysis.
Side effects
- Exceeds 200-row limit — request rejected
- Missing required fields (row_id, date, dataset_group) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size outside 2–200 range — rejected
- Field value outside allowed numeric range — validation error
- Empty rows array — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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