Deep learning model (LSTM) with temporal and 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 or 1.5 months) 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?

 A: It seems like you could do this relatively easily using a model that has MLP and RNN parts.
Suppose that your time-series data is $A$. You compute some RNN output $r(A)$ that uses $A$ as the input; this can return a vector since you have, say, $i$ RNN units in the final layer.
Suppose that your "tabular" data is $B$. You compute some MLP that has output $m(B)$; this can return a vector since you have, say, $j$ nodes in the final layer of this MLP.
Now you have two vectors which, in some sense, encode the data contained in the tabular and time-series components of your data. You can concatenate these vectors to make a new vector of length $i +j=k$. This is the input to another fully-connected layer in your network, or possibly more than one. Then the output is just whatever your usual output is.
The reason that I think this could work is that you process the time-series and tabular pieces with models which are appropriate for their respective types, and then combine the results in a way which permits both formats to be used together.
But this could be hard to train. This would not be my first choice of a model. Instead, I would prefer to try using either a tabular or a time series model by itself, and making a determination of whether or not either simpler model is suitable.
Note that this structure trains all parts of the network, the MLP, the RNN and the "combiner" part, all at once. This does not require you to train three separate networks.
This is easy enough to do in most modern neural network software, such as Keras.
