# 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` | 5-bin histogram mode | |

2 | `DN_HistogramMode_10` | `mode_10` | 10-bin histogram mode | |

3 | `DN_OutlierInclude_p_001_mdrmd` | `outlier_timing_pos` | Positive outlier timing | |

4 | `DN_OutlierInclude_n_001_mdrmd` | `outlier_timing_neg` | Negative outlier timing | |

5 | `ﬁrst1e_acf_tau` | `acf_timescale` | First $1/e$ crossing of the ACF | |

6 | `ﬁrstMin_acf` | `acf_first_min` | First minimum of the ACF | |

7 | `SP_Summaries_welch_rect_area_5_1` | `low_freq_power` | Power in lowest 20% frequencies | |

8 | `SP_Summaries_welch_rect_centroid` | `centroid_freq` | Centroid frequency | |

9 | `FC_LocalSimple_mean3_stderr` | `forecast_error` | Error of 3-point rolling mean forecast | |

10 | `FC_LocalSimple_mean1_tauresrat` | `whiten_timescale` | Change in autocorrelation timescale after incremental differencing | |

11 | `MD_hrv_classic_pnn40` | `high_fluctuation` | Proportion of high incremental changes in the series | |

12 | `SB_BinaryStats_mean_longstretch1` | `stretch_high` | Longest stretch of above-mean values | |

13 | `SB_BinaryStats_diff_longstretch0` | `stretch_decreasing` | Longest stretch of decreasing values | |

14 | `SB_MotifThree_quantile_hh` | `entropy_pairs` | Entropy of successive pairs in symbolized series | |

15 | `CO_HistogramAMI_even_2_5` | `ami2` | Histogram-based automutual information (lag 2, 5 bins) | |

16 | `CO_trev_1_num` | `trev` | Time reversibility | |

17 | `IN_AutoMutualInfoStats_40_gaussian_fmmi` | `ami_timescale` | First minimum of the AMI function | |

18 | `SB_TransitionMatrix_3ac_sumdiagcov` | `transition_variance` | Transition matrix column variance | |

19 | `PD_PeriodicityWang_th001` | `periodicity` | Wang's periodicity metric | |

20 | `CO_Embed2_Dist_tau_d_expfit_meandiff` | `embedding_dist` | Goodness of exponential fit to embedding distance distribution | |

21 | `SC_FluctAnal_2_rsrangeﬁt_50_1_logi_prop_r1` | `rs_range` | Rescaled range fluctuation analysis (low-scale scaling) | |

22 | `SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1` | `dfa` | 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 |

Last modified 1mo ago