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Promotion Observations — Base-100 Series + Explicit Missing-Value Fill
x402
Promotion Observations — 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 2026-10-25).
Converts promotion observation data (discount_amount, units_sold) into a Base-100 indexed time series and fills missing values using zero, mean, or median imputation, returning both reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Promotion observations — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill for supplied promotion observations records (discount_amount, units_sold). 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 where the chosen field (discount_amount or units_sold) is normalized relative to the specified base row, and (2) the same dataset with missing values explicitly filled using the selected method (zero, mean, or median). Both reports cover all supplied rows up to 200.
Example use cases
Benchmarking discount trends across store groups
“I have 4 weeks of promotion records for two store groups with discount_amount and units_sold. Can you index the discount_amount to Base-100 starting at row 0 and fill any nulls with the median so I can compare trends across groups?”
Cleaning missing units sold before reporting
“My promotion dataset has some null units_sold values — can you fill those in using mean imputation and also produce a Base-100 indexed series for units_sold starting from the first row? I've got about 150 rows across several store groups.”
Normalizing promotion data for cross-period comparison
“I want to normalize my retail promotion observations so discount_amount is indexed to 100 at row 5 as the baseline, and any missing values should be replaced with zero. Can you run that on my dataset of 80 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-05-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular retail promotion data with discount_amount and/or units_sold fields and need both Base-100 normalization and explicit missing-value imputation in a single call. It is ideal for small-to-medium datasets (up to 200 rows) where you want reproducible, locally computed analytics without any external lookups, predictions, or live data dependencies. Prefer this over general-purpose analytics tools when you specifically need the combination of indexed series creation and null-fill in one payment.
Side effects
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format not matching YYYY-MM-DD pattern — schema validation error
- base_index references a row index out of range for the supplied dataset
- Unrecognized fill method value outside zero/mean/median enum
- Empty rows array — minItems violation
- Numeric values outside the allowed range (±1,000,000,000,000)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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