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Feature overview table

Note that all catch22 features are statistical properties of the z-scored time series—they aim to focus on properties of the time-ordering of the data and are insensitive to the raw values in the time series.
In the table below 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) categorized into broad conceptual groupings.
#
Feature name
Short name
Category
Description
1
DN_HistogramMode_5
mode_5
​Distribution shape​
5-bin histogram mode
2
DN_HistogramMode_10
mode_10
​Distribution shape​
10-bin histogram mode
3
DN_OutlierInclude_p_001_mdrmd
outlier_timing_pos
​Extreme event timing​
Positive outlier timing
4
DN_OutlierInclude_n_001_mdrmd
outlier_timing_neg
​Extreme event timing​
Negative outlier timing
5
ﬁrst1e_acf_tau
acf_timescale
​Linear autocorrelation​
First 1/e1/e1/e
 crossing of the ACF
6
ﬁrstMin_acf
acf_first_min
​Linear autocorrelation​
First minimum of the ACF
7
SP_Summaries_welch_rect_area_5_1
low_freq_power
​Linear autocorrelation​
Power in lowest 20% frequencies 
8
SP_Summaries_welch_rect_centroid
centroid_freq
​Linear autocorrelation​
Centroid frequency
9
FC_LocalSimple_mean3_stderr
forecast_error
​Simple forecasting​
Error of 3-point rolling mean forecast
10
FC_LocalSimple_mean1_tauresrat
whiten_timescale
​Incremental differences​
Change in autocorrelation timescale after incremental differencing
11
MD_hrv_classic_pnn40
high_fluctuation
​Incremental differences​
Proportion of high incremental changes in the series
12
SB_BinaryStats_mean_longstretch1
stretch_high
​Symbolic​
Longest stretch of above-mean values
13
SB_BinaryStats_diff_longstretch0
stretch_decreasing
​Symbolic​
Longest stretch of decreasing values
14
SB_MotifThree_quantile_hh
entropy_pairs
​Symbolic​
Entropy of successive pairs in symbolized series
15
CO_HistogramAMI_even_2_5
ami2
​Nonlinear autocorrelation​
Histogram-based automutual information (lag 2, 5 bins)
16
CO_trev_1_num
trev
​Nonlinear autocorrelation​
Time reversibility
17
IN_AutoMutualInfoStats_40_gaussian_fmmi
ami_timescale
​Nonlinear autocorrelation​
First minimum of the AMI function
18
SB_TransitionMatrix_3ac_sumdiagcov
transition_variance
​Symbolic​
Transition matrix column variance
19
PD_PeriodicityWang_th001
periodicity
​Linear autocorrelation structure​
Wang's periodicity metric
20
CO_Embed2_Dist_tau_d_expfit_meandiff
embedding_dist
​Other​
Goodness of exponential fit to embedding distance distribution
21
SC_FluctAnal_2_rsrangeﬁt_50_1_logi_prop_r1
rs_range
​Self-affine scaling​
Rescaled range fluctuation analysis (low-scale scaling)
22
SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1
dfa
​Self-affine scaling​
Detrended fluctuation analysis (low-scale scaling)
And in some cases, in which scale and spread of the raw time-series values may be relevant to class differences, the two simple distributional moment features (using the catch24 flag in the software implemenations) can be added:
Feature name
Short name
Description
DN_Mean
mean
Mean
DN_Spread_Std
std
Standard deviation
​

#	Feature name	Short name	Category	Description
1	`DN_HistogramMode_5`	`mode_5`	Distribution shape	5-bin histogram mode
2	`DN_HistogramMode_10`	`mode_10`	Distribution shape	10-bin histogram mode
3	`DN_OutlierInclude_p_001_mdrmd`	`outlier_timing_pos`	Extreme event timing	Positive outlier timing
4	`DN_OutlierInclude_n_001_mdrmd`	`outlier_timing_neg`	Extreme event timing	Negative outlier timing
5	`ﬁrst1e_acf_tau`	`acf_timescale`	Linear autocorrelation	First $1/e$ crossing of the ACF
6	`ﬁrstMin_acf`	`acf_first_min`	Linear autocorrelation	First minimum of the ACF
7	`SP_Summaries_welch_rect_area_5_1`	`low_freq_power`	Linear autocorrelation	Power in lowest 20% frequencies
8	`SP_Summaries_welch_rect_centroid`	`centroid_freq`	Linear autocorrelation	Centroid frequency
9	`FC_LocalSimple_mean3_stderr`	`forecast_error`	Simple forecasting	Error of 3-point rolling mean forecast
10	`FC_LocalSimple_mean1_tauresrat`	`whiten_timescale`	Incremental differences	Change in autocorrelation timescale after incremental differencing
11	`MD_hrv_classic_pnn40`	`high_fluctuation`	Incremental differences	Proportion of high incremental changes in the series
12	`SB_BinaryStats_mean_longstretch1`	`stretch_high`	Symbolic	Longest stretch of above-mean values
13	`SB_BinaryStats_diff_longstretch0`	`stretch_decreasing`	Symbolic	Longest stretch of decreasing values
14	`SB_MotifThree_quantile_hh`	`entropy_pairs`	Symbolic	Entropy of successive pairs in symbolized series
15	`CO_HistogramAMI_even_2_5`	`ami2`	Nonlinear autocorrelation	Histogram-based automutual information (lag 2, 5 bins)
16	`CO_trev_1_num`	`trev`	Nonlinear autocorrelation	Time reversibility
17	`IN_AutoMutualInfoStats_40_gaussian_fmmi`	`ami_timescale`	Nonlinear autocorrelation	First minimum of the AMI function
18	`SB_TransitionMatrix_3ac_sumdiagcov`	`transition_variance`	Symbolic	Transition matrix column variance
19	`PD_PeriodicityWang_th001`	`periodicity`	Linear autocorrelation structure	Wang's periodicity metric
20	`CO_Embed2_Dist_tau_d_expfit_meandiff`	`embedding_dist`	Other	Goodness of exponential fit to embedding distance distribution
21	`SC_FluctAnal_2_rsrangeﬁt_50_1_logi_prop_r1`	`rs_range`	Self-affine scaling	Rescaled range fluctuation analysis (low-scale scaling)
22	`SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1`	`dfa`	Self-affine scaling	Detrended fluctuation analysis (low-scale scaling)

Feature name	Short name	Description
`DN_Mean`	`mean`	Mean
`DN_Spread_Std`	`std`	Standard deviation

Overview

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Distribution shape

Last modified 7mo ago