Inferring linear and nonlinear Interaction networks using neighborhood support vector machines
Kamel Jebreen, Mohamad Motasem Nawaf, Amjad S. Barham, Badih Ghattas · 2021 International Conference on Engineering and Emerging Technologies (ICEET) · 2021
In this paper we consider modeling 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.