cyclicityanalysis 1.0.2

Performs Cyclicity Analysis on A Collection of Time-Series

Cyclicity Analysis of Time-Series

This repository contains a working implementation of Cyclicity Analysis, which is a pattern recognition technique for analyzing the leader follower dynamics of multiple time-series.

Full documentation and an example Jupyter notebook are available in the GitHub repository.

Requirements

Download Python >=3.7

Installation

pip3 install cyclicityanalysis

Usage

from cyclicityanalysis.orientedarea import *
from cyclicityanalysis.coom import *

df = pd.DataFrame([[0, 1], [1, 0], [0, 0]], columns=['0', '1'])


oa = OrientedArea(df)
# Returns the lead lag matrix of df as a dataframe
lead_lag_df = oa.compute_lead_lag_df()

coom = COOM(lead_lag_df)
# Returns leading eigenvector of lead lag matrix as a numpy array
leading_eigenvector = coom.get_leading_eigenvector()
lead_lag_df , leading_eigenvector

References

• Cyclicity in Multivariate Time-series and Applications to Functional MRI data : paper
• Dissociating Tinnitus Patients from Healthy Controls using Resting-state Cyclicity Analysis and Clustering : paper
• Slow Cortical Waves through Cyclicity Analysis : paper
• Comparing Cyclicity Analysis With Pre-established Functional Connectivity Methods to Identify Individuals and Subject Groups Using Resting State fMRI : paper