# From Time Series to Networks in R with the ts2net Package

arXiv: https://arxiv.org/abs/2208.09660 · HTML: https://mlsanity.com/paper/2208.09660/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Time Series Analysis via Network Science: Concepts and Algorithms](https://mlsanity.com/paper/2110.09887/) (2021)
- [MuxViz: A Tool for Multilayer Analysis and Visualization of Networks](https://mlsanity.com/paper/1405.0843/) (2014)

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

- [The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making](https://mlsanity.com/paper/2301.08970/) (2023)

## Abstract (arXiv)

Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. R is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. However, no single package provides the necessary methods to transform time series into networks. This paper presents ts2net, an R package for modeling one or multiple time series into networks. The package provides the time series distance functions that can be easily computed in parallel and in supercomputers to process larger data sets and methods to transform distance matrices into networks. Ts2net also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. Together with other packages, ts2net permits using network science and graph mining tools to extract information from time series.