PyRQA—Conducting recurrence quantification analysis on very long time series efficiently. (July 2017)

• Record Type: Journal Article
• Authors: Rawald, Tobias; Sips, Mike; Marwan, Norbert
• Is Part Of: Computers & geosciences. Volume 104(2017)
• Page Start: 101
• Page End: 108
• Publication Date: 2017-07
• Subjects: Time series analysis -- Recurrence analysis -- RQA -- Software -- Distributed processing -- Parallel algorithm; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2016.11.016
• Languages: English
• ISSNs: 0098-3004

Abstract

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 . Abstract : Highlights: Conduct RQA on time series exceeding one million data points in short time. Computing approach exploits the parallel computing capabilities of GPUs. Computing approach distributes the processing across multiple compute devices. Usage of OpenCL allows to employ compute devices with different architectures. Free and open-source under version 2.0 of the Apache License.