# 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 | --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://time-series-features.gitbook.io/catch22-features/feature-overview-table.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.