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Dataset Storage Analytics: Trailing Mean Series + Base-100 Series
x402
Dataset Storage Analytics: Trailing Mean Series + Base-100 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).
Computes trailing moving average series and base-100 indexed series for dataset storage metrics (used_mb, row_count) from up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Trailing mean series + Base-100 series: Trailing mean series + Base-100 series 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 mean series showing smoothed values of the selected metric (used_mb or row_count) over the specified window, and (2) a base-100 indexed series normalizing the metric relative to the specified base row index. Both reports are computed locally on the submitted data with no external lookups.
Example use cases
Storage growth trend smoothing
“I have 60 daily dataset storage records with used_mb values across two groups — can you compute a trailing mean with a window of 7 days so I can see the smoothed usage trend?”
Base-100 normalization for storage comparison
“Take my dataset storage rows going back 90 days and produce a base-100 indexed series for row_count starting from index 0, so I can compare growth relative to the starting point.”
Dual analytics on storage snapshot
“Run both a trailing mean series and a base-100 series on this batch of 150 dataset storage records for used_mb, using a window of 5 and base index 0 — I need both reports in one call.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-147?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a rolling/trailing average and a base-100 normalized index computed together on dataset storage records (used_mb or row_count) in a single API call, without requiring any external data sources or live lookups. Ideal for offline or batch analytics workflows where cost efficiency matters and you want two complementary time-series transformations at once.
Side effects
- Fewer than the window size rows provided causes incomplete trailing mean values at the start of the series
- Invalid date format (not YYYY-MM-DD) triggers schema validation error
- base_index out of range for the submitted row count returns an error
- Supplying more than 200 rows exceeds the maxItems limit and is rejected
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) cause validation failures
- Null values in the selected metric field may produce gaps or NaN entries in the series
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer | |
| base_index | integer |
Tags
x402
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