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From Time Series to Networks in R with the ts2net Package
Social and Information Networks · Submitted 20 Aug 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This paper presents ts2net, an R package for modeling one or multiple time series into networks.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Network science established itself as a prominent tool for modeling time series and complex systems. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | This modeling process consists of transforming a set or a single time series into a network. | background 0.94 | 0.05 | 0.03 | asserted 1.00 |
| s2 | Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. | background 0.94 | 0.05 | 0.03 | hedged possibility 0.94 |
| s3 | R is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s4 | However, no single package provides the necessary methods to transform time series into networks. | gap 1.00 | 0.16 | 0.05 | asserted 1.00 |
| s5 | This paper presents ts2net, an R package for modeling one or multiple time series into networks. | contribution 1.00 | 0.85 | 0.50 | asserted 1.00 |
| s6 | 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. | contribution 0.68 | 0.72 | 0.09 | asserted 0.99 |
| s7 | Ts2net also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. | contribution 0.83 | 0.74 | 0.07 | asserted 1.00 |
| s8 | Together with other packages, ts2net permits using network science and graph mining tools to extract information from time series. | contribution 0.61 | 0.27 | 0.23 | asserted 0.76 |
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The paper
Original abstract
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.
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1 arXiv paper cite this in 3 sentences
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Uses its method or tool 1 paper
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To transform our set of time series into networks, we employed the `ts2net` [ref] library in R, with Dynamic Time Warping (DTW) being the distance function used, as justified in [ref].
Dynamics of Fisheries in the Azores Islands: A Network Analysis Approach · Methods · 1.00
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