  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 seconds using
PyRQA.
