# Selecting the right time series model [closed]

Using Python, I am trying to predict the future sales count of a product, using historical sales data. I am also trying to predict these counts for various groups of products.

For example, my columns looks like this:

Date Sales_count Department Item Color

8/1/2018, 50, Homegoods, Hats, Red_hat


If I want to build a model that predicts the sales_count for each Department/Item/Color combo using historical data (time), what is the best model to use?

If I do Linear regression on time against sales, how do I account for various categories? Can I group them?

Would I instead use multilinear regression, treating the various categories as independent variables?