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Cache Observations Trailing Mean Series
x402
Cache Observations Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Computes a rolling/trailing mean series over cache hit and miss observations (up to 200 records) for local descriptive analytics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Cache observations - Trailing mean series: Trailing mean series for Cache observations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded trailing mean series report.
Output
Returns a trailing mean series report containing smoothed rolling average values for the selected metric field (hits or misses) across up to 200 supplied cache observation records, grouped by cache name and date, using the specified window size. No forecasting or external enrichment is included — results are purely local descriptive analytics.
Example use cases
Smoothing daily cache hit trends
“I have 30 days of cache hit counts for three cache groups and want to smooth them out — can you run a trailing mean with a window of 7 on the hits field so I can see the trend without the day-to-day noise?”
Cache miss anomaly dampening
“My cache miss data is really spiky — can you apply a rolling mean with window size 5 to my misses observations across groups A and B so I can see a cleaner baseline?”
Comparing cache group performance over time
“I want to compare rolling average hit rates for group-a versus group-b over the past two weeks. Can you run a trailing mean series with a window of 3 on the hits field for all my records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-cache-observations-rolling?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap ($0.01 USDC) rolling/trailing mean series computed locally over cache performance data (hits or misses) without any external enrichment, forecasting, or model inference. It is ideal for smoothing noisy cache metric time series before visualization or anomaly detection, particularly when working with grouped cache observations up to 200 records.
Side effects
- Fewer than 2 rows supplied — minimum item count not met
- Window size exceeds the number of supplied rows — insufficient data for computation
- Invalid date format (must be YYYY-MM-DD)
- Metric values outside allowed range (±1,000,000,000,000)
- Missing required fields (row_id, cache, date, hits, misses)
- Exceeding 200 row maximum — payload too large
- Invalid field enum value (must be 'hits' or 'misses')
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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