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Brand Mention Inventories — Trailing Mean Series + Two-Measure Correlation
x402
Brand Mention Inventories — Trailing Mean Series + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Computes a trailing moving-average series and a two-measure Pearson/rank correlation between mention_count and source_count for supplied brand mention inventory records (up to 200 rows).
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied brand mention inventories records (mention_count, source_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: (1) a trailing moving-average time series for the selected primary field (mention_count or source_count) computed with the specified window size, and (2) a two-measure correlation analysis between mention_count and source_count. Results are based entirely on the supplied rows — no external data is fetched.
Example use cases
Brand campaign performance smoothing
“I've got 90 days of daily mention_count and source_count records for 'group-acme' and 'group-rival' — can you compute a 14-day trailing mean series on mention_count and also give me the correlation between mention_count and source_count across all rows?”
PR spike vs source diversity analysis
“After our product launch I have 60 rows of mention data by date and company group. Run a trailing mean with a window of 5 on mention_count and tell me how correlated mention_count is with source_count — I want to know if mention spikes are driven by source breadth.”
Quarterly brand health review
“Here are 200 rows of brand mention inventory records across four company groups for Q3. Please generate a trailing mean series with window 10 on source_count and also run the two-measure correlation report comparing mention_count and source_count.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-150?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have brand mention inventory data (with mention_count and source_count fields) and need both a rolling/trailing mean time series AND a correlation analysis between the two measures in a single low-cost call. It is purpose-built for brand monitoring analytics on supplied data with no external lookup, making it ideal for offline or batch statistical reporting on pre-collected brand data.
Side effects
- Fewer than 2 rows supplied — minItems validation failure
- Window size larger than number of rows — insufficient data for trailing mean
- Missing required fields (row_id, date, mention_count, source_count) — schema validation error
- Date strings not matching YYYY-MM-DD pattern — rejected by pattern validator
- More than 200 rows submitted — maxItems exceeded
- Null values in both measure columns — correlation undefined or NaN result
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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