# Time-Varying Interaction Estimation Using Ensemble Methods

arXiv: https://arxiv.org/abs/1906.10746 · HTML: https://mlsanity.com/paper/1906.10746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics](https://mlsanity.com/paper/2203.11743/) (2022)

## Abstract (arXiv)

Directed information (DI) is a useful tool to explore time-directed interactions in multivariate data. However, as originally formulated DI is not well suited to interactions that change over time. In previous work, adaptive directed information was introduced to accommodate non-stationarity, while still preserving the utility of DI to discover complex dependencies between entities. There are many design decisions and parameters that are crucial to the effectiveness of ADI. Here, we apply ideas from ensemble learning in order to alleviate this issue, allowing for a more robust estimator for exploratory data analysis. We apply these techniques to interaction estimation in a crowded scene, utilizing the Stanford drone dataset as an example.