# Inferring linear and nonlinear Interaction networks using neighborhood support vector machines

## TL;DR

This paper introduces two novel methods for modeling variable interactions in high-dimensional time series data, utilizing neighborhood support vector machines and a restricted Bayesian network, demonstrating their effectiveness through simulations.

## Contribution

It proposes two new approaches—SVM-based neighborhood modeling and a Bayesian network—for capturing interactions in complex time series data, extending existing methods.

## Key findings

- Effective in modeling linear and nonlinear interactions
- Demonstrated through simulation studies
- Outperforms traditional methods in accuracy

## Abstract

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.