The goal is to devise a data generation methodology that, given a data sample, can approximate the stochastic process that generated it. The methodology can be useful in many contexts where we need to share data while preserving user privacy.

There are known literature for data generation based on Bayesian neural networks/hidden Markov models that are restricted to static and propositional data. We focused on time-evolving data and preference data.

We studied  two aspects:

1. the generator able to produce realistic data, having the same properties as the original one.
2. how to inject drift within the data generation process in a controlled manner.