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Brand Mention Inventories — Trailing Mean Series + Secondary-Measure Weighted Mean
x402
Brand Mention Inventories — Trailing Mean Series + Secondary-Measure Weighted Mean 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 mean time series and a source_count-weighted mean for supplied brand mention inventory records (mention_count, source_count) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Trailing mean series + Secondary-measure weighted mean: Trailing mean series + Secondary-measure weighted mean 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 for the supplied brand mention inventory rows: (1) a trailing mean time series over the specified window for the chosen field (mention_count or source_count), and (2) a secondary-measure (source_count) weighted mean of the primary field — both computed locally with no external lookups.
Example use cases
Smoothing weekly brand mention spikes
“I have daily brand mention data for three company groups over the last month — can you calculate a trailing mean series with a window of 7 on mention_count, plus a source_count-weighted mean? I'll give you the rows with row_id, company_group, date, mention_count, and source_count.”
Comparing brand visibility across competitors
“I want to compare brand mention trends across our top competitor groups. Can you run a trailing mean with window size 5 and a source-count-weighted mean on the mention_count field for these 150 rows of brand mention inventory data?”
Monitoring campaign impact on brand mentions
“We just ran a marketing campaign and I want to see how brand mentions have changed — can you produce a trailing mean time series (window of 4) and a weighted mean using source_count as the weight, on the mention_count field across these campaign period records?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-151?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 source-count-weighted mean in a single low-cost call. Ideal for offline or batch brand analytics pipelines where no live lookup or prediction is needed and cost efficiency matters ($0.01 for 2 reports on up to 200 rows).
Side effects
- Fewer than 1 or more than 200 rows supplied — request rejected
- Window size exceeds number of rows — trailing mean may return insufficient data points
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, company_group, date, mention_count, source_count) — validation failure
- Null values in mention_count or source_count may affect weighted mean accuracy
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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