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 $1/e$ 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