Perdicting user outcome from multi-variate 3D time-series dataset

Hi there,
I work with a time-stamped log data and I want to make a model that could generalize user “habits”. For example, I have a huge dataset which made during an edX MOOC course (Online Video-Based Education - 6 weeks). Every user has log history and based on this I want to predict the user outcome after the first, second, etc. week. Another example is I have a customer purchase history, and I want to predict the amount of the next purchase.
In the first example, I have only categorical data (play, stop, pause, site viewing, etc.), while in the second I have continual and discrete variables.
As an initial project I used GRU and LSTM, but I want to use a more sophisticated model.
My goal is to make a regression (prediction) on time-series data.
My first idea to use BERT for the first and InceptionTime for the second.
Do you have any suggestions?
InceptionTime is made for Time-series classification. It can use for regression?