Unleashing the power of simulation-based inference: an application to complex stochastic processes.

In this era of huge data availability, data-driven approaches are affirming themselves as one of the dominant paradigm in model identification. Due to their capability to fit acquired data, these models exhibit flexibility and the ability to cope with undiscovered knowledge. This paper proposes a method to overcome existing limitations in the model and parameter identification of complex stochastic Time-Series, enabling the identification of processes characterisable according to the Gaussian Mixture Model. More concretely, this paper aims to define methods for learning and classifying the model of complex stochastic processes.