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.