Sequential network change detection with its applications to ad impact relation analysis

We are concerned with the issue of tracking changes of variable dependencies from multivariate time series. Conventionally, this issue has been addressed in the batch scenario where the whole data set is given at once, and the change detection must be done in a retrospective way. This paper addresses this issue in a sequential scenario where multivariate data are sequentially input and the detection must be done in a sequential fashion. We propose a new method for sequential tracking of variable dependencies. In it we employ a Bayesian network as a representation of variable dependencies. The key ideas of our method are: (1) we extend the theory of dynamic model selection, which has been developed in the batch-learning scenario, into the sequential setting, and apply it to our issue, (2) we conduct the change detection sequentially using dynamic programming per a window where we employ the Hoeffding’s bound to automatically determine the window size. We empirically demonstrate that our proposed method is able to perform change detection more efficiently than a conventional batch method. Further, we give a new framework of an application of variable dependency change detection, which we call Ad Impact Relation analysis (AIR). In it, we detect the time point when a commercial message advertisement has given an impact on the market and effectively visualize the impact through network changes. We employ real data sets to demonstrate the validity of AIR.