Stability-based Dynamic Bayesian Network method for dynamic data mining

In this article we introduce a new stability-based dynamic Bayesian network method for dynamic systems represented by their time series. Based on the Grow Shrink algorithm and the stability of the network through time, new variables and arcs could be added to the network in order to generate missing data or predict future values. The concept of stability in the network is maintained through a stability matrix which contains learned values that indicate the strength of dependencies between variables along the time. Moreover, we present the application of the proposed method to deal with the problem of prediction in a real-life air quality case study, in which we try to predict the level of Carbon monoxide in the air, comparing between the results obtained using the proposed method and those obtained using the Vector Autoregression model.