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?