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Save Files Analysis — Trailing Mean Series + Explicit Missing-Value Fill
x402
Save Files Analysis — Trailing Mean Series + 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 2026-11-06).
Computes trailing moving-average series and fills missing values (via zero, mean, or median) for save file metrics (save_kb, write_ms) across up to 200 rows, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied save files records (save_kb, write_ms). 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: (1) a trailing mean series for the chosen field (save_kb or write_ms) computed over the specified window, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median), both applied to the supplied rows.
Example use cases
Smoothing noisy save-size data
“I've got 120 rows of daily game save file records with save_kb measurements, but there are gaps where the value is null. Can you fill the missing save_kb values using mean imputation and then compute a trailing mean series with a window of 7 so I can see the smoothed trend?”
Write latency trend analysis
“Here are 80 rows of game area save telemetry including write_ms timestamps. Some write_ms entries are missing — fill them with zeros and give me a trailing mean series over a window of 3 so I can spot performance regressions by game area.”
Pre-processing save data for reporting
“I need to clean up this batch of 200 save file records before sending them to my reporting dashboard — fill any null save_kb values using median and compute a rolling 10-day trailing mean so the chart lines don't jump around.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular save-file telemetry (save_kb or write_ms) that needs both missing-value imputation and trailing moving-average smoothing in a single call, with no external data sources required. It is ideal for pre-processing game analytics data before visualization or downstream ML pipelines, especially when rows contain nulls and you want a clean time-series output. Prefer alternatives if you need live lookups, forecasting, or metrics beyond save_kb/write_ms.
Side effects
- Fewer than 1 or more than 200 rows supplied returns a validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Window size smaller than 2 or larger than 200 is rejected
- Unknown fill method (not zero/mean/median) causes a schema error
- Rows missing required fields (row_id, game_area, date) are rejected
- Numeric values outside ±1,000,000,000,000 bounds cause validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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