For forecasting a product metric, which data attribute types are appropriate to represent numerical measures and ordered categories?

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When forecasting a product metric, you want data attributes that can capture both quantities and ordered categories. Numerical attributes represent numeric measures such as units sold, revenue, or page views—values you can add, average, and compare. Ordinal attributes represent categories that have a natural order, like customer satisfaction levels (low to high) or product tier (basic, standard, premium). This combination lets forecasting models use precise quantities while also leveraging the relative order of categories to detect trends and effects.

The other options don’t fit as well because they aren’t appropriate data attribute types for this purpose. Boolean data is just true/false and misses the numeric scale; an array is a collection rather than a single attribute type; standard deviation, constraints, mean, and median are statistics or rules, not fundamental data attribute types to model with.