As the title suggests, I have a multivariate longitudinal dataset (also called panel data). (I have over 100.000 observations. The time period is X years. This means that for every year I have the values of the same variables. So I can see how every variable fluctuates over these X years for a specific observation). It is a binary classification case. So the output is either 0 or 1. I was able to figure out how to do this in order to be able to feed it into deep learning models (RNN, LSTM, GRU, CNN) because you can specify it when define the shape of the data.
How did I structure my data for the DL models: The columns represented the variables, a row was added for each year and at the end of the last year was mentionned the 1 or 0 . Then again X rows for the next observation. Let's say you have 5 years of data for an observation, then you get 5 rows per observation. (-> timesteps = 5) I mainly got this from the following page: https://machinelearningmastery.com/how-to-develop-convolutional-neural-network-models-for-time-series-forecasting/ (go to title 'Multiple Input Series').
Now, I also want to test other machine learning methods like Random Forest ( AdaBoost, XGboost, ...) but I don't find anything useful in literature... Has someone suggestions on how to deal with the data in these cases?