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  PyRQA - Conducting recurrence quantification analysis on very long time series efficiently

Rawald, T., Sips, M., Marwan, N. (2017): PyRQA - Conducting recurrence quantification analysis on very long time series efficiently. - Computers and Geosciences, 104, 101-108.
https://doi.org/10.1016/j.cageo.2016.11.016

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 Creators:
Rawald, T.1, Author
Sips, M.1, Author
Marwan, Norbert2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: PyRQA is a software package that efficiently conducts recurrence quantification analysis (RQA) on time series consisting of more than one million data points. RQA is a method from non-linear time series analysis that quantifies the recurrent behaviour of systems. Existing implementations to RQA are not capable of analysing such very long time series at all or require large amounts of time to calculate the quantitative measures. PyRQA overcomes their limitations by conducting the RQA computations in a highly parallel manner. Building on the OpenCL framework, PyRQA leverages the computing capabilities of a variety of parallel hardware architectures, such as GPUs. The underlying computing approach partitions the RQA computations and enables to employ multiple compute devices at the same time. The goal of this publication is to demonstrate the features and the runtime efficiency of PyRQA. For this purpose we employ a real-world example, comparing the dynamics of two climatological time series, and a synthetic example, reducing the runtime regarding the analysis of a series consisting of over one million data points from almost eight hours using state-of-the-art RQA software to roughly 69 s using PyRQA.

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 Dates: 2017
 Publication Status: Finally published
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cageo.2016.11.016
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7852
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Model / method: Research Software Engineering (RSE)
Model / method: Open Source Software
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Computers and Geosciences
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 104 Sequence Number: - Start / End Page: 101 - 108 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals86