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Time-dependent Canonical Correlation Analysis

Project description

Time-dependent Canonical Correlation Analysis

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.

Getting Started

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 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 for details of packages requirements. 

Installing from GitHub

Download the packages by using git clone

python install

If you experience problems related to installing the dependency Matplotlib on OSX, please see

Intro to our package

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 *


The examples subfolder includes simulations provided in the paper. 

Running the tests

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

Built With

  • Python 2.7


  • Python 2.7



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

Project details

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