# Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks

arXiv: https://arxiv.org/abs/1810.02950 · HTML: https://mlsanity.com/paper/1810.02950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2018.

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

- [Generative Correlation Manifolds: Generating Synthetic Data with Preserved Higher-Order Correlations](https://mlsanity.com/paper/2510.21610/) (2025)

## Abstract (arXiv)

In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of time series that have strong linear dependence among themselves, with the requirement that each time series makes a significant contribution to the linear dependence. We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network. Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach. Using our proposed framework, we demonstrate the utility of multipoles in discovering new physical phenomena in two scientific domains: climate science and neuroscience. In particular, we discovered several multipole relationships that are reproducible in multiple other independent datasets and lead to novel domain insights.