TY - JOUR
T1 - Temporal state change Bayesian networks for modeling of evolving multivariate state sequences
T2 - model, structure discovery and parameter estimation
AU - Mrowca, Artur
AU - Gyrock, Florian
AU - Günnemann, Stephan
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Latent variable models
KW - Multivariate state sequences
KW - Probabilistic processes
KW - Temporal Bayesian networks
UR - http://www.scopus.com/inward/record.url?scp=85118551998&partnerID=8YFLogxK
U2 - 10.1007/s10618-021-00807-y
DO - 10.1007/s10618-021-00807-y
M3 - Article
AN - SCOPUS:85118551998
SN - 1384-5810
VL - 36
SP - 240
EP - 294
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
IS - 1
ER -