Modeling Dynamic Systems with Memory: What Is the Right Time-Order?
Anna Łupińska-Dubicka, Marek J. Drużdżel · D-Scholarship@Pitt (University of Pittsburgh) · 2011
Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal inuences of the first order, i.e., inuences between neighboring time steps. This choice is a convenient approximation inuenced by the existence of efficient algorithms for first order models and limitations of available tools. We focus on the question whether constructing higher time-order models is worth the effort when the underlying system's memory goes beyond the current state. We present the results of an experiment with a series of DBN models monitoring woman's monthly cycle. We show that higher order models are significantly more accurate. However, we have also observed overfitting and a resulting decrease in accuracy when the time order chosen is too high.