Julia package to calculate selected main measures of the recurrence
quantification analysis (RQA). The calculation is performed in an
highly efficient but very approximative way using the algorithm
described in Spiegel et al, 2016.
The RQA approximation corresponds to non-embedded RQA and using
maximum norm.
Note: Depending on the time series and its length, the results can deviate
much from standard (exact) RQA implementation. Be careful when using this approach!
Installation
From GitHub
Since this package is not yet registered in the Julia General registry, you can install it directly from GitHub:
using Pkg
Pkg.add(url="https://github.com/pucicu/apRQA")
Development Installation
If you want to modify the package or contribute to development:
using Pkg
Pkg.develop(url="https://github.com/pucicu/apRQA")
Or clone the repository and install locally:
git clone https://github.com/pucicu/apRQA.git
cd apRQA
Then in Julia:
using Pkg
Pkg.activate(".")
Pkg.instantiate()
Requirements
Julia ≥ 1.6
Dependencies (automatically installed):
LinearAlgebra
StatsBase
Quick Start
using apRQA
using Random
# Generate example data
Random.seed!(42)
x = rand(10000)
# Parameters
ε = 0.1 # similarity threshold
minL = 2 # minimum line length
# Compute approximate RQA measures
results = approximate_rqa(x, ε, minL)
RR, DET, L, LAM = results
println("RR: $(results[:RR])") # Recurrence Rate
println("DET: $(results[:DET])") # Determinism
println("L: $(results[:L])") # Average diagonal line length
println("LAM: $(results[:LAM])") # Laminarity
Testing
Run the test suite:
using Pkg
Pkg.test("apRQA")
Reference
S. Spiegel, D. Schultz, N. Marwan: Approximate Recurrence Quantification Analysis (aRQA) in Code of Best Practice, In: Recurrence Plots and Their Quantifications: Expanding Horizons, Eds.: C. L. Webber, Jr. and C. Ioana and N. Marwan, Springer, Cham, 113–136 (2016). DOI:10.1007/978-3-319-29922-8_6
Copyright
Norbert Marwan\
Potsdam Institute for Climate Impact Research\
11/2025
