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Brand Mention Inventories — Base-100 Series + Explicit Missing-Value Fill
x402
Brand Mention Inventories — Base-100 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 a base-100 indexed time series and fills missing values (zero, mean, or median) 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 — Base-100 series + Explicit missing-value fill: Base-100 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 base-100 indexed series for the specified field (mention_count or source_count) relative to the chosen baseline row, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports cover up to 200 input rows and are computed locally without external lookups.
Example use cases
Rebasing brand mentions for competitor comparison
“I have daily mention_count data for two brand groups from Q4 2025. Can you convert it to a base-100 series starting at the first row and fill any nulls with the median so I can compare growth trends side by side?”
Imputing missing source counts before reporting
“My source_count data has some null entries across 150 rows for three company groups. Can you fill in those missing values using the mean and also give me the base-100 indexed series starting from row 0?”
Normalizing mention inventory for dashboard visualization
“I've got 180 rows of mention_count records for group-a and group-b across several months — please rebase them to 100 starting at index 5 and fill any gaps with zero so I can plug the output straight into our dashboard.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected brand mention inventory data (mention_count or source_count) organized by date and company group, and need both a base-100 normalized time series and explicit missing-value imputation in a single call. Ideal for offline/batch data cleaning and normalization workflows where no live data fetch, prediction, or external enrichment is needed. Best suited for datasets up to 200 rows; for larger datasets, split into multiple calls.
Side effects
- More than 200 rows supplied — request exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- base_index out of range (0–199) relative to actual row count
- All values for the target field are null, making mean/median fill undefined
- row_id missing or empty string causes required field validation error
- Numeric values outside ±1,000,000,000,000 bounds rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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