  In this paper, we consider modelling interaction between a set of variables
in the context of time series and high dimension. We suggest two approaches.
The first is similar to the neighborhood lasso when the lasso model is replaced
by a support vector machine (SVMs). The second is a restricted Bayesian network
adapted for time series. We show the efficiency of our approaches by
simulations using linear, nonlinear data set and a mixture of both.
