Julia API
This is the class and function reference of catch22.jl. Please refer to the user guide for further details as the class and function specifications may not be sufficient to give full context.
Last updated
This is the class and function reference of catch22.jl. Please refer to the user guide for further details as the class and function specifications may not be sufficient to give full context.
Last updated
The Catch22 module provides functionality for extracting the catch22 feature set from time series data.
A collection of feature extraction functions, their names, keywords, and descriptions.
A hierarchichalFeatureSet in which one or more evaluated features are non-independent, relying on some (potentially) common preprocessing transformations; e.g. the partial autocorrelation at successive lags. Feature sets of this type detect any common preprocessing transformations, as to only perform each calculation once. Catch22.jluses this to compute the zscore transformation, required by all catch22 features.
Further details on the custom types used by the Catch22.jl package can be found at .
Evaluate all catch22 features for a time series vector 𝐱 or the columns of an array X. Can be indexed by feature names (as symbols) to return a subset of the available features.
Evaluate all catch22 features along with the mean (DN_Mean) and standard deviation (DN_Spread_Std) for a time series vector 𝐱 or the columns of an array X.
Evaluate all catch22 features with shortened names for a time series vector 𝐱 or the columns of an array X.
Evaluate all catch22 features with shortened names, along with the mean (mean) and standard deviation (std) for a time series vector 𝐱 or the columns of an array X.
DN_Mean
𝐱 (Vector)
Float64
Computes the mean of the input time series.
DN_Spread_Std
𝐱 (Vector)
Float64
Computes the standard deviation of the input time series.
DN_HistogramMode_5
𝐱 (Vector)
Float64
Computes the mode of the input time series using a histogram with 5 bins.
DN_HistogramMode_10
𝐱 (Vector)
Float64
Computes the mode of the input time series using a histogram with 10 bins.
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The Catch22 module also provides direct access to the individual feature extraction methods. These methods can be called directly using Catch22.{name}, where {name} is the name of the feature method (as given by the long name in the ).
