catch22: CAnonical Time-series CHaracteristics
catch22 GitHub
  • Welcome to catch22
    • Citing catch22
    • Publications using catch22
  • LANGUAGE-SPECIFIC DOCS
    • Python
    • MATLAB
    • R
    • Julia
    • C-compiled
  • INFORMATION ABOUT CATCH22
    • Feature Descriptions
      • Feature Overview Table
      • Distribution shape
      • Extreme event timing
      • Linear autocorrelation structure
      • Nonlinear autocorrelation
      • Symbolic
      • Incremental differences
      • Simple forecasting
      • Self-affine scaling
      • Other
    • API Reference
      • Python API
      • Julia API
      • R API
      • MATLAB API
    • Contributing to catch22
      • Contributor Code of Conduct
    • Related Packages
    • License
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  • catch22_all
  • Syntax
  • Description
  • Examples
  • Input Arguments
  • Output Arguments
  • GetAllFeatureNames
  • Syntax
  • Description
  • Examples
  • Input Arguments
  • Output Arguments
  • catch22_DN_HistogramMode_5
  • Syntax
  • Description
  • Examples
  • Input Arguments
  • Output Arguments
  1. INFORMATION ABOUT CATCH22
  2. API Reference

MATLAB API

This is the class and function reference of catch22. Please refer to the user guide for further details as the class and function specifications may not be sufficient to give full context.

catch22_all

Automatically run every time series feature calculation included in the catch22/24 set.


Syntax

[featureValues, featureNamesLong, featureNamesShort] = catch22_all(data, doCatch24)

Description

[featureValues, featureNamesLong, featureNamesShort] = catch22_all(data, doCatch24) returns a 24x1 array of feature outputs, a 24x1 array of long feature names and 24x1 array of short feature names for the input time series, data.


Examples

Compute catch22 features

Compute catch22 features on a Gaussian i.i.d. random number sequence.

tsData = rand(100, 1) % data as a column vector

% compute catch22 features
[featureValues, featureNamesLong, featureNameShort] = catch22_all(data, false)

% compute cactch24 features
[featureValues, featureNamesLong, featureNameShort] = catch22_all(data)

Input Arguments

data - time series as column vector

Input univariate time series, specified as a column vector.

Data Types: double

doCatch24 - Include Mean and Std. Deviation

Whether to include mean and standard deviation (a total of 24 features). Default is true.

Data Types: logical


Output Arguments

featureValues

Computed feature values for the input data.

Data Type: cell array

featureNamesLong

Long feature names corresponding to the feature values.

Data Type: cell array

featureNamesShort

Short feature names corresponding to the feature values.

Data Type: cell array


GetAllFeatureNames

Get all feature names for the catch22/24 feature set.


Syntax

[featureNamesLong, featureNamesShort] = GetAllFeatureNames(doCatch24)

Description

[featureNamesLong, featureNamesShort] = GetAllFeatureNames(doCatch24) retrieves all feature names for the catch22 (or catch24) feature set.


Examples

Retrieve catch22/24 feature names
% get catch24 feature names
[featureNamesLong24,featureNamesShort24] = GetAllFeatureNames(true)
% get catch22 feature names
[featureNamesLong22,featureNamesShort22] = GetAllFeatureNames(false)

Input Arguments

doCatch24

Whether to include mean and standard deviation as features. Default is true.

Data Types: logical


Output Arguments

featureNamesLong

Long feature names corresponding to the feature values.

Data Type: cell array

featureNamesShort

Short feature names corresponding to the feature values.

Data Type: cell array



catch22_DN_HistogramMode_5

Measures the mode of the data vector using histogram with 5 bins.

Syntax

% individual feature method of interest, e.g., DN_HistogramMode_5
featureVal = catch22_DN_HistogramMode_5(data)

Description

featureVal = catch22_DN_HistogramMode_5(data) computes the mode of the data vector using a histogram with 5 bins.

Examples

Compute DN_HistogramMode_5 for time series
tsData = rand(1, 100) % time series as a row vector
featureVal = catch22_DN_HisogramMode_5(tsData)

Input Arguments

data - time series row vector

Input univariate time series, specified as a row vector.

Data Types: double

Output Arguments

featureVal - feature output

Output corresponding to the feature method.

Data Type: double

Note: All catch22/24 feature methods expect the same input arguments (and data types) and return the same output arguments (and data types).


PreviousR APINextContributing to catch22

Last updated 1 year ago

Note: This is a typical example of how an individual feature method is called. All features are called in this way and accept the same data types as inputs while returning the same data types as outputs. You can call each feature individually using its corresponding long name as per the , e.g., catch22_CO_f1ecac.

table of features
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