This package implements the Bipartite PCPG (biPCPG) algorithm [1], a generalisation of the Partial Correlation Planar Graph (PCPG) algorithm [2]. The PCPG is a correlation-filtering method for the construction of networks intended for use on multivariate time series datasets with a single sample. The biPCPG framework generalises this approach to allows its use on similar datasets containing multi-sample multivariate time series.
The biPCPG package offers three main tools:
- Handling the dataset, via the
bipcpg.utils.utils.reshape_year_matrices_to_time_series_matricesfunction. - Applying the PCPG, via the
bicpg.pcpg.PCPGclass. - Performing a bootstrap on the PCPG network's edges, via the
bipcpg.bootstsrap.get_bootstrap_valuesfunction.
The documentation is hosted here. We recommend having a look at the tutorial to get started.
| [1] | Saenz de Pipaon Perez C, Zaccaria A, Di Matteo T. Asymmetric Relatedness from Partial Correlation. Entropy. 2022; 24(3):365. <https://doi.org/10.3390/e24030365> |
| [2] | Kenett DY, Tumminello M, Madi A, Gur-Gershgoren G, Mantegna RN, Ben-Jacob E. Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market. PLoS ONE. 2010; 5(12):e15032. <https://doi.org/10.1371/journal.pone.0015032> |