# Predictions

We use the demand forecast models to predict the sales volume for all products for the discounts available (as defined in the constraints). 

Due to how inference works on time series, there needs to be no duplication of products for each time step. Thus, a separate dataset needs to be created for each discount scenario.

![split_markdown.png](pmTk3L6OrbYH)

we split the diffrent discounts into separeted datasets

![stack_markdown.png](G7Fb9DhUuUgq)

We stack them back with the historical data

![infer_markdown.png](COmEsB295Uvr)

and then we run the inference on each dataset (including for the zero discount and constant discount scenario).

## Model Evaluation

The models come directly from demand forecasts. We are playing on the price and discount parameters of the products to predict their effect.
If those parameters positively impact the predicted demand, we can more accurately represent the price elasticity.

It's also recommended to perform exploratory analysis on your data to ensure you have good coverage of markdowns on your historical data. Without it, the models will not be able to perform well for unknown markdowns.
