Data Driven Profiling of Dynamic System Behavior Using Hidden Markov Model Based Combined Unsupervised and Supervised Classification

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

1999 SIGART/AAAI Doctoral Consortium

Dynamic systems are often best characterized by a combination of static and temporal features, with the static features describing time-invariant properties of the system, and the temporal features capturing dynamic aspects of the system. Our goal is to construct context based temporal behavior models of dynamic systems using information from both types of features. Our dynamic system profiling framework consists of three main steps: (i) model generation, (ii) model validation, and (iii) model interpretation. Model generation step can be further decomposed into two components: (ia) temporal model generation, and (ib) context generation.