Training a LSTM/any other deep learning model with temporal as well as non temporal attributes I'm working on a project to predict the usage of all the files(rough frequency of usage) in a filesystem(a company server on which 100s of company employees are active) in near future(say the next 1 month) based on the metadata of the file system for past 6 months. I've got the following attributes about the files with me :


The temporal sequence of file usage for last 6 months(whenever the
file was read/written/modified and by whom).
All the users who are on the server and can access the files.
Last modified/written/read epoch time and by whom.
File creation epoch time and by whom.
Any compliance regulations on the file(whether the file contains any
confidential data).
Size, name, extension, version, type of the file.
The number of users who can access the file.
File path.
The total number of times accessed.
Permitted users.


Now, I plan to use LSTM but for standard LSTMs, the input is temporal sequence only. However, all the attributes that I have seem significant in predicting the future usage of the file.


How should I also make use of the attributes of the file that I have?
Should I train a Feedforward Neural Network, disregarding the fact
that it usually fails on temporal sequences?
How should I proceed?
Does a variant of LSTM exist that can take into account the
attributes of the file as well and predict the usage of the file in
near future?
Do I need to use MLP and LSTM together like a hybrid?
Like you said, you can proceed your temporal data with a recurrent network like LSTM. I suggest you simply concatenate your other features to the output of the LSTM (with return_sequence = False). Then add some dense layers (1 could be enough) : this will output the probability of belonging to your classes.