ts2vg: Time series to visibility graphs¶
The Python ts2vg package provides high-performance algorithm implementations to build visibility graphs from time series data, as first introduced by Lucas Lacasa et al. in 2008 1.
The visibility graphs and some of their properties (e.g. degree distributions) are computed quickly and efficiently even for time series with millions of observations. An efficient divide-and-conquer algorithm is used to compute the graphs whenever possible 3.
The latest released ts2vg version is available at the Python Package Index (PyPI) and can be easily installed by running:
pip install ts2vg
For other advanced uses, to build ts2vg from source Cython is required.
Supported graph types¶
Root graph types¶
Weighted Visibility Graphs (via the
Directed Visibility Graphs (via the
Parametric Visibility Graphs 5 (via the
Limited Penetrable Visibility Graphs (LPVG) 4 6 (via the
Note that multiple graph variations can be combined and used simultaneously.
Usage and reference documentation for ts2vg can be found at carlosbergillos.github.io/ts2vg.
To build a visibility graph from a time series do:
from ts2vg import NaturalVG ts = [1.0, 0.5, 0.3, 0.7, 1.0, 0.5, 0.3, 0.8] vg = NaturalVG() vg.build(ts) edges = vg.edges
The time series passed (
ts) can be any one-dimensional iterable, such as a list or a
numpy 1D array.
By default, the input observations are assumed to be equally spaced in time.
Alternatively, a second 1D iterable (
xs) can be provided for unevenly spaced time series.
Horizontal visibility graphs can be obtained in a very similar way:
from ts2vg import HorizontalVG ts = [1.0, 0.5, 0.3, 0.7, 1.0, 0.5, 0.3, 0.8] vg = HorizontalVG() vg.build(ts) edges = vg.edges
If we are only interested in the degree distribution of the visibility graph
we can pass
only_degrees=True to the
This will be more efficient in time and memory than storing the whole graph.
vg = NaturalVG() vg.build(ts, only_degrees=True) ks, ps = vg.degree_distribution
Directed graphs can be obtained by using the
and weighted graphs can be obtained by using the
vg1 = NaturalVG(directed="left_to_right") vg1.build(ts) vg2 = NaturalVG(weighted="distance") vg2.build(ts) vg3 = NaturalVG(directed="left_to_right", weighted="distance") vg3.build(ts) vg4 = HorizontalVG(directed="left_to_right", weighted="h_distance") vg4.build(ts)
For more information and options see: Examples and API Reference.
Interoperability with other libraries¶
The graphs obtained can be easily converted to graph objects from other common Python graph libraries such as igraph, NetworkX and SNAP for further analysis.
The following methods are provided:
vg = NaturalVG() vg.build(ts) g = vg.as_networkx()
Command line interface¶
ts2vg can also be used as a command line program directly from the console:
ts2vg ./timeseries.txt -o out.edg
For more help and a list of options run:
ts2vg is licensed under the terms of the MIT License.
Lucas Lacasa et al., “From time series to complex networks: The visibility graph”, 2008.
Lucas Lacasa et al., “Horizontal visibility graphs: exact results for random time series”, 2009.
Xin Lan et al., “Fast transformation from time series to visibility graphs”, 2015.
T.T Zhou et al., “Limited penetrable visibility graph for establishing complex network from time series”, 2012.
I.V. Bezsudnov et al., “From the time series to the complex networks: The parametric natural visibility graph”, 2014
Qi Xuan et al., “CLPVG: Circular limited penetrable visibility graph as a new network model for time series”, 2021