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Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation