Modeling Dynamic Systems with Memory: What Is the Right Time-Order?

Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal influences of the first order, i.e., influences between neighboring time steps. This choice is a convenient approximation influenced 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.