Inferring linear and nonlinear Interaction networks using neighborhood support vector machines
This work addresses interaction modeling in high-dimensional time series, but it appears incremental as it builds on existing methods like neighborhood lasso and Bayesian networks.
The paper tackled the problem of modeling interactions among variables in high-dimensional time series by proposing two methods: a neighborhood SVM approach and a restricted Bayesian network adapted for time series. The result demonstrated efficiency through simulations on linear, nonlinear, and mixed datasets, though no concrete numbers were provided.
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.