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https://github.com/felixpatzelt/scorr

Fast and flexible two- and three-point correlation analysis for time series using spectral methods.

https://github.com/felixpatzelt/scorr

Science Score: 20.0%

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    CITATION.cff file
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    Links to: arxiv.org
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    1 of 2 committers (50.0%) from academic institutions
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    Low similarity (10.9%) to scientific vocabulary

Keywords

correlation correlations python scientific spectral-methods time-series
Last synced: 5 months ago · JSON representation

Repository

Fast and flexible two- and three-point correlation analysis for time series using spectral methods.

Basic Info
  • Host: GitHub
  • Owner: felixpatzelt
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 10.5 MB
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 5
  • Open Issues: 1
  • Releases: 0
Topics
correlation correlations python scientific spectral-methods time-series
Created over 8 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog License

README.rst 

scorr
=====

Fast two- and three-point correlation analysis for time series
using spectral methods.

The calculations are FFT-based for optimal performance and offer many options 
for normalisation, mean removal, averaging, and zero-padding. In particular, 
averaging over pandas groups of different sizes (e.g. different days) is 
supported.
    
======================  ======================================================
Function                Synopsis
======================  ======================================================
acorr                   Calculate autocorrelation or autocovariance
acorr_grouped_df        Calculate acorr for pandas groups and average
corr_mat                Convert correlation vector to matrix
fft2x                   Calculate cross-bispectrum
fftcrop                 Return cropped fft or correlation
get_nfft                Find a good FFT segment size for pandas groups of 
                        different sizes 
padded_x3corr_norm      Normalise and debias three-point cross-correlations
padded_xcorr_norm       Normalise and debias two-point cross-correlations
x3corr                  Calculate three-point cross-correlation matrix
x3corr_grouped_df       Calculate x3corr for pandas groups and average
xcorr                   Calculate two-point cross-correlation or covariance
xcorr_grouped_df        Calculate xcorr for pandas groups and average
xcorrshift              Convert xcorr output so lag zero is centered
======================  ======================================================

The algorithms to calculate three-point correlations and details of daily
averaging over high-frequency trading data are described in:
	
    Patzelt, F. and Bouchaud, J-P. (2017):
    Nonlinear price impact from linear models. 
    Journal of Statistical Mechanics: Theory and Experiment, 12, 123404. 
    Preprint at `arXiv:1708.02411 `_.

More code from the same publication is released in the `priceprop 
`_ package. 

Please find further 
explanations in the docstrings and in the examples 
directory. 


Installation
------------

	pip install scorr
	

Dependencies (automatically installed)
--------------------------------------

    - Python 2.7 or 3.6
    - NumPy
    - SciPy
    - Pandas    
   
    
Optional Dependencies required only for the examples (pip installable)
----------------------------------------------------------------------

    - Jupyter
    - Matplotlib
    - colorednoise

Owner

  • Name: Felix Patzelt
  • Login: felixpatzelt
  • Kind: user
  • Company: @moia-dev

Data Science and Interdisciplinary Physics - Socioeconomic and Neural Systems

GitHub Events

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  • Push event: 1
  • Fork event: 1
  • Create event: 1
Last Year
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Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 12
  • Total Committers: 2
  • Avg Commits per committer: 6.0
  • Development Distribution Score (DDS): 0.083
Top Committers
Name Email Commits
Felix Patzelt f****x@n****e 11
Felix Patzelt 7****t@u****m 1
Committer Domains (Top 20 + Academic)
neuro.uni-bremen.de: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 15 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
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  • felixpatzelt (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 34 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 3
  • Total maintainers: 1
pypi.org: scorr

Fast and flexible two- and three-point correlation analysis for time series using spectral methods.

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 34 Last month
Rankings
Dependent packages count: 10.0%
Forks count: 15.3%
Stargazers count: 17.1%
Average: 20.9%
Dependent repos count: 21.7%
Downloads: 40.3%
Maintainers (1)
felixpatzelt
Last synced: 6 months ago

Dependencies

setup.py pypi
  • numpy *
  • pandas *
  • scipy *