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Network science established itself as a prominent tool for modeling timeseries and complex systems. This modeling process consists of transforming aset or a single time series into a network. Nodes may represent complete timeseries, segments, or single values, while links define associations orsimilarities between the represented parts. R is one of the main programminglanguages used in data science, statistics, and machine learning, with manypackages available. However, no single package provides the necessary methodsto transform time series into networks. This paper presents ts2net, an Rpackage for modeling one or multiple time series into networks. The packageprovides the time series distance functions that can be easily computed inparallel and in supercomputers to process larger data sets and methods totransform distance matrices into networks. Ts2net also provides methods totransform 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 extractinformation from time series.

From Time Series to Networks in R with the ts2net Package