I'm working on a project to predict the usage of all the files in a filesystem in near future based on the metadata of the file system for past 6 months. I've got the following attributes about the files with me :

  1. The temporal sequence of file usage for last 6 months(whenever the file was read/written/modified and by whom).
  2. All the users who are on the server and can access the files.
  3. Last modified/written/read epoch time and by whom.
  4. File creation epoch time and by whom.
  5. Any compliance regulations on the file(whether the file contains any confidential data).
  6. Size, name, extension, version, type of the file.
  7. Number of users who can access the file.
  8. File path.
  9. Total number of times accessed.
  10. 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 NN and LSTM together like a hybrid?

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