Time Series Forecasting with Neural Network (mixed data types)
I guess I answered the question in the comments, so here goes.

Most ML models cannot deal with categorical values. A common way to solve this is to use one-hot encoding, also known as dummy variables. For very possible value of your categorical variable you create a column which is 0 unless this row has this category, then it is 1. It is possible to remove one of the categories since it is a linear combination of the other dummy variables (if all are 0, the last one must be 1).

The downside of this method is that it increases the dimensionality of your feature space. If you have enough data to support this or not that many categories that is not a problem. There are other alternatives, like taking the average feature of every category and adding that to your features as opposed to the categorical feature.