Catch22.jl
A Julia package wrapping catch22, which is a set of 22 time-series features shown by Lubba et al. (2019) to be performant in a range of time-series classification problems.
The catch22 repository provides these 22 features, originally coded in Matlab as part of the hctsa toolbox, as C functions (in addition to Matlab and Python wrappers). This package simply uses Julia's ccall to wrap these C functions from a shared library that is accessed through catch22_jll and compiled by the fantastic BinaryBuilder package.
Usage
Installation
using Pkg
Pkg.add("Catch22")
using Catch22
Input time series
The input time series can be provided as a Vector{Float64} or Array{Float64, 2}. If an array is provided, the time series must occupy its columns. For example, this package contains a few test time series from catch22:
๐ฑ = Catch22.testdata[:testSinusoid] # a Vector{Float64}
X = randn(1000, 10) # an Array{Float64, 2} with 10 time series
Evaluating a feature
A list of features (as symbols) can be obtained with getnames(catch22) and their short descriptions with getdescriptions(catch22). Each feature can be evaluated for a time series array or vector with the catch22 FeatureSet. For example, the feature DN_HistogramMode_5 can be evaluated using:
f = catch22[:DN_HistogramMode_5](๐ฑ) # Returns a scalar Float64
๐ = catch22[1](X) # Returns a 1ร10 Matrix{Float64}
All features are returned as Float64's, even though some may be constrained to the integers.
Alternatively, functions that calculate each feature individually are exported. DN_HistogramMode_5 can be evaluated with:
f = DN_HistogramMode_5(๐ฑ)
Evaluating a feature set
All catch22 features can be evaluated with:
๐ = catch22(๐ฑ)
F = catch22(X)
If an array is provided, containing one time series in each of N columns, then a 22รN FeatureArray of feature values will be returned (a subtype of AbstractDimArray).
A FeatureArray has most of the properties and methods of an Array but is annotated with feature names that can be accessed with getnames(F).
If a vector is provided (a single time series) then a vector of feature values will be returned as a FeatureVector, a one-dimensional FeatureArray.
Finally, note that since catch22 is a FeatureSet it can be indexed with a vector of feature names as symbols to calculate a FeatureArray for a subset of catch22. For details on the Feature, FeatureSet and FeatureArray types check out the package docs.
Single-threaded performance
Calculating features for a single time series of a given length:
Multithreaded performance
Calculating features for 100 time series of a given length:
