Selecting the right time series model 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?
You are saying "For various group of products" and this is your answer. 

Forecast each group of product independently, then split the group total on individual products. The reason for this strategy are that it is easier and more accurate to forecast big numbers than rare cases. An other reason is that by grouping similar product, you have chances to capture niceties like new products cannibalizing old products.