> For the complete documentation index, see [llms.txt](https://time-series-features.gitbook.io/catch22-features/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://time-series-features.gitbook.io/catch22-features/feature-overview-table.md). # 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 |