Canonical Correlation Analysis is a technique in multivariate data analysis for finding correlation and pairs of vectors that maximizes the correlation between a set of paired variables. Many important problems involve recoding time-dependent observations. In order to understand the coupling dynamics between the two sources, spot trends, detect anomalies, in this paper, we introduce the time-dependent canonical correlation analysis (TDCCA), a method of inferring time-dependent canonical vectors of paired variables. For more details of our paper, please refer to our paper.

@article{cao2019time, title={Time-dependent Canonical Correlation Analysis for Multilevel Time Series}, author={Cao, Xuefei and Ke, Jun and Sandstede, Björn and Luo, Xi}, journal={bioRxiv}, pages={650101}, year={2019}, publisher={Cold Spring Harbor Laboratory} }

We provide both simulation examples used in our paper. The main computation algorithm is not added into the class method for the convenience of algorithm development for multi-view data. The package saves all the analysis to the given folder and saves the preprocessed data into the hdf5 file. The parallel computing with multi-cores is also allowed and tested in Linux system. This package also provides other algorithms to optimize the function, including cvxpy naive optimization, cvxpy naive admm optimization and two other admm algorithms. For details, see the admm_computation.py file. However, these functions have not been tested thoroughly.

These instructions will get you a copy of the project up running on your local machine for development and testing purposes.

This package is also published in pypi. For a quick installation, try

pip install tdcca 

What things you need to install the software and how to install them

See setup.py for details of packages requirements. 

Download the packages by using git clone https://github.com/xuefeicao/tdcca.git

python setup.py install

If you experience problems related to installing the dependency Matplotlib on OSX, please see https://matplotlib.org/faq/osx_framework.html

After installing our package locally, try to import tdcca in your python environment and learn about package's function. Note: our package name in pypi is tdcca. It is recommended that users scale the data before running our package.

from tdcca import *
help(multi_sim)

The examples subfolder includes simulations provided in the paper. 

The test has been conducted in both Linux and Mac Os.

• Python 2.7

• Python 2.7

• Xuefei Cao - Maintainer - (https://github.com/xuefeicao)
• Jun Ke
• Björn Sandstede (http://www.dam.brown.edu/people/sandsted/)
• Xi Luo (http://bigcomplexdata.com/)

This project is licensed under the MIT License - see the LICENSE file for details