TS2G2 stands for "timeseries to graphs and back". The library implements a variety of strategies to convert timeseries into graphs, and convert graphs into sequences.
stream = TimeseriesArrayStream([2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3])
timeseries = Timeseries(stream)
g = timeseries.to_graph(NaturalVisibilityGraphStrategy())
sequence = g.to_sequence(RandomWalkSequenceGenerationStrategy(), sequence_length=500)
For a more detailed example, look at the Amazon stocks demo.
Many of the methods implemented in this library are described in Silva, Vanessa Freitas, et al. "Time series analysis via network science: Concepts and algorithms." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11.3 (2021): e1404. Nevertheless, the library also includes additional techniques found in other works from the scientific literature.
This package is being developed as part of the Graph-Massivizer project. The package is a joint effort between the Jožef Stefan Institute, the University of Twente, the Vrije Universiteit Amsterdam, the University of Klagenfurt, the University of Bologna, and Peracton.
# | Visibility Graph | Graph type | Constraints | |||||
---|---|---|---|---|---|---|---|---|
Undirected | Directed | Weighted | ||||||
Penetration | Angle | |||||||
1 | Natural Visibility Graph | X | X | X | X | X | ||
2 | Horizontal Visibility Graph | X | X | X | X | X | ||
3 | Difference Visibility Graph | |||||||
4 | Quantile Graph | X | X | |||||
5 | Ordinal Partition Graph | X | X |
# | Visibility Graph | Graph type | Constraints | |||||
---|---|---|---|---|---|---|---|---|
Undirected | Directed | Weighted | ||||||
Penetration | Angle | |||||||
1 | Natural Visibility Graph | ref | ref | ref | ref, ref | |||
2 | Horizontal Visibility Graph | ref | ref | ref | ref, ref | |||
3 | Difference Visibility Graph | |||||||
4 | Quantile Graph | ref | ||||||
5 | Ordinal Partition Graph |
Graphs are converted back to timeseries by sampling node values from the graph following different strategies. The following strategies have been implemented so far:
- random node
- random node neighbour
- random node degree
- random walk
- random walk with restart
- random walk with jump
When using this work for research purposes, we would appreciate it if the following references could be included:
Below we provide a curated list of papers related to our research in this area: