Feature Overview Table
A highlevel summary table of all catch22 features.
Note: All catch22 features are statistical properties of the zscored time series  they aim to focus on the properties of the timeordering of the data and are insensitive to the raw values in the time series.
In the following table, we give the original feature name (from the Lubba et al. (2019) paper), and a shorter name more suitable for use in feature descriptions. Features are also (loosely) categorised into broader conceptual groupings.
Each feature is listed according to the order in which it, along with its associated value, is returned by catch22 e.g., the first feature returned (i.e., #1) is always DN_HistogramMode_5.
#  Feature name  Short name  Category  Description 

1 

 5bin histogram mode  
2 

 10bin histogram mode  
3 

 Positive outlier timing  
4 

 Negative outlier timing  
5 

 
6 

 First minimum of the ACF  
7 

 Power in lowest 20% frequencies  
8 

 Centroid frequency  
9 

 Error of 3point rolling mean forecast  
10 

 Change in autocorrelation timescale after incremental differencing  
11 

 Proportion of high incremental changes in the series  
12 

 Longest stretch of abovemean values  
13 

 Longest stretch of decreasing values  
14 

 Entropy of successive pairs in symbolized series  
15 

 Histogrambased automutual information (lag 2, 5 bins)  
16 

 Time reversibility  
17 

 First minimum of the AMI function  
18 

 Transition matrix column variance  
19 

 Wang's periodicity metric  
20 

 Goodness of exponential fit to embedding distance distribution  
21 

 Rescaled range fluctuation analysis (lowscale scaling)  
22 

 Detrended fluctuation analysis (lowscale scaling) 
Catch24 (Catch22 + Mean + Std)
In some cases, in which scale and spread of the raw timeseries values may be relevant to class differences, the two simple distributional moment features (using the catch24
flag in the software implementations) can be added. This will result in 24 features being calculated: the catch22 features in addition to the mean and standard deviation.
#  Feature name  Short name  Description 

23 

 Mean 
24 

 Standard deviation 
Feature Dependencies
Although the selection framework used to generate the catch22 feature set included a step to reduce redundancy, it was not designed to generate an independent set of features.
Pairwise Spearman Correlations
Below is an example of the generic nonindependence of features. We have plotted the Spearman correlation coefficient between all pairs of features, quantifying the similarity of their outputs across a diverse range of 1000 empirical time series:
We find a large cluster of features sensitive to the autocorrelation of a time series.
We also find a small cluster of two highly correlated features,
DN_HistogramMode_5
andDN_HistogramMode_10
, which measure the mode of the zscired timeseries distribution using different numbers of bins.
This dependency structure should be taken in mind when interpreting the result of catch22 analyses: Does your dataset exhibit any of these generic dependencies, or some unique dependencies?
PC Loadings
Below is a similar plot, but with colour overlayed according to the weights onto the first three principal components:
Broadly,
The first two principal components capture different aspects of the autocorrelation structure.
The third principal component captures different aspects of the distribution symmetry.
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