Modeling dynamic processes with memory by higher order temporal models

Abstract

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. In this paper, 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 in which we successively introduce higher order DBN models monitoring woman’s monthly cycle and measure the accuracy of these models in estimating the fertile period around the day of ovulation. We show that higher order models are more accurate than first order models. However, we have also observed over-fitting and a resulting decrease in accuracy when the time order chosen is too high.