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Brand Mention Inventories — Trailing Mean Series + Explicit Missing-Value Fill
x402
Brand Mention Inventories — 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 2026-10-16).
Computes trailing mean time series and fills missing values for brand mention inventory data (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 + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill 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 mean series computed over the specified window for the target field, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Results cover up to 200 input rows and are produced entirely from supplied data using local analytical engines with no external lookups.
Example use cases
Smooth noisy brand mention trends
“I have 150 rows of daily brand mention data across two company groups with dates and mention_count values — can you compute a trailing mean series with a window of 14 and fill any missing mention_count values using the mean method so I can see a cleaner trend?”
Impute missing source counts for reporting
“My brand monitoring dataset has some null source_count values across different dates and company groups. Can you run the missing-value fill using the median method on the source_count field for up to 200 rows so I can get a complete dataset for my report?”
Compare smoothed mention curves per group
“I have brand mention inventory records for two company groups spanning 90 days — please calculate a trailing mean series with a window of 5 on the mention_count field, filling any gaps with zero, so I can compare the smoothed curves side by side.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to both smooth brand mention time series data via a trailing moving average AND handle missing values in a single paid API call. It bundles two analytical operations (trailing mean + missing-value imputation) for mention_count or source_count fields at a low flat cost. Prefer it over general-purpose analytics tools when working with brand mention inventory records grouped by company and date, especially when the dataset fits within 200 rows and no external data enrichment or prediction is needed.
Side effects
- Input array exceeds 200 rows — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Window size outside 2–200 range — schema validation failure
- Missing required fields (row_id, date, company_group) — request rejected
- No rows supplied (empty array) — validation error
- Unsupported fill method value — enum validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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