SANGE – Stochastic Automata Networks Generator. A tool to efficiently predict events through structured Markovian models

Abstract

The use of stochastic formalisms, such as Stochastic Automata Networks (SAN), can be very useful for statistical prediction and behavior analysis. Once well fitted, such formalisms can generate probabilities about a target reality. These probabilities can be seen as a statistical approach of knowledge discovery. However, the building process of models for real world problems is time consuming even for experienced modelers. Furthermore, it is often necessary to be a domain specialist to create a model. This work illustrates a new method to automatically learn simple SAN models directly from a data source. This method is encapsulated in a tool called SAN GEnerator (SANGE). This new model fitting method is powerful and relatively easy to use; therefore this can grant access to a much broader community to such powerful modeling formalisms.

SANGE – Stochastic Automata Networks Generator. A tool to efficiently predict events through structured Markovian models

“…Once the time ticks of interest are identified, the second step is to determine the possible values for the variables in order to define the stochastic model. This process is summarized in an example with three time series in Figure 2 and Figure 3 showing the identification of 11 time ticks (a) and the three succession of values for variables v (1) , v (2) and v (3) .…”