This PipeOp extracts the 22 (or 24) canonical time series characteristics (catch22) from the target
variable. For more details, see Rcatch22::catch22_all(), which is called internally on the ordered
target vector.
The catch22 set is a low-redundancy subset of the hctsa features, selected for their performance across a diverse collection of time series classification tasks, but applicable as general-purpose features for other tasks such as regression.
For other time series feature extractors, see PipeOpFcstTsfeats and PipeOpFcstFeasts.
Parameters
The parameters are the parameters inherited from mlr3pipelines::PipeOpTaskPreproc, as well as:
catch24::logical(1)
IfTRUE, additionally compute the mean and standard deviation (the catch24 set). DefaultFALSE.
Naming
The new columns are named {target}_catch22_{feature}. If the target was called "y" and the feature is
"DN_HistogramMode_5", the corresponding new column will be called "y_catch22_DN_HistogramMode_5".
Super classes
mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpFcstCatch22
Methods
PipeOpFcstCatch22$new()
Initializes a new instance of this Class.
Usage
PipeOpFcstCatch22$new(id = "fcst.catch22", param_vals = list())Examples
library(mlr3pipelines)
task = tsk("airpassengers")
po = po("fcst.catch22")
out = po$train(list(task))[[1L]]
out$head()
#> passengers passengers_catch22_DN_HistogramMode_5 passengers_catch22_DN_HistogramMode_10 passengers_catch22_CO_f1ecac passengers_catch22_CO_FirstMin_ac
#> <num> <num> <num> <num> <num>
#> 1: 112 -1.03778 -0.8218858 28.49835 8
#> 2: 118 -1.03778 -0.8218858 28.49835 8
#> 3: 132 -1.03778 -0.8218858 28.49835 8
#> 4: 129 -1.03778 -0.8218858 28.49835 8
#> 5: 121 -1.03778 -0.8218858 28.49835 8
#> 6: 135 -1.03778 -0.8218858 28.49835 8
#> passengers_catch22_CO_HistogramAMI_even_2_5 passengers_catch22_CO_trev_1_num passengers_catch22_MD_hrv_classic_pnn40 passengers_catch22_SB_BinaryStats_mean_longstretch1
#> <num> <num> <num> <num>
#> 1: 0.7175964 -0.002839309 0.8951049 48
#> 2: 0.7175964 -0.002839309 0.8951049 48
#> 3: 0.7175964 -0.002839309 0.8951049 48
#> 4: 0.7175964 -0.002839309 0.8951049 48
#> 5: 0.7175964 -0.002839309 0.8951049 48
#> 6: 0.7175964 -0.002839309 0.8951049 48
#> passengers_catch22_SB_TransitionMatrix_3ac_sumdiagcov passengers_catch22_PD_PeriodicityWang_th0_01 passengers_catch22_CO_Embed2_Dist_tau_d_expfit_meandiff
#> <num> <num> <num>
#> 1: 0.1666667 11 0.6712426
#> 2: 0.1666667 11 0.6712426
#> 3: 0.1666667 11 0.6712426
#> 4: 0.1666667 11 0.6712426
#> 5: 0.1666667 11 0.6712426
#> 6: 0.1666667 11 0.6712426
#> passengers_catch22_IN_AutoMutualInfoStats_40_gaussian_fmmi passengers_catch22_FC_LocalSimple_mean1_tauresrat passengers_catch22_DN_OutlierInclude_p_001_mdrmd
#> <num> <num> <num>
#> 1: 5 0.03846154 0.7847222
#> 2: 5 0.03846154 0.7847222
#> 3: 5 0.03846154 0.7847222
#> 4: 5 0.03846154 0.7847222
#> 5: 5 0.03846154 0.7847222
#> 6: 5 0.03846154 0.7847222
#> passengers_catch22_DN_OutlierInclude_n_001_mdrmd passengers_catch22_SP_Summaries_welch_rect_area_5_1 passengers_catch22_SB_BinaryStats_diff_longstretch0
#> <num> <num> <num>
#> 1: -0.6736111 0.9485463 5
#> 2: -0.6736111 0.9485463 5
#> 3: -0.6736111 0.9485463 5
#> 4: -0.6736111 0.9485463 5
#> 5: -0.6736111 0.9485463 5
#> 6: -0.6736111 0.9485463 5
#> passengers_catch22_SB_MotifThree_quantile_hh passengers_catch22_SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 passengers_catch22_SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1
#> <num> <num> <num>
#> 1: 1.50827 0.7692308 0.2820513
#> 2: 1.50827 0.7692308 0.2820513
#> 3: 1.50827 0.7692308 0.2820513
#> 4: 1.50827 0.7692308 0.2820513
#> 5: 1.50827 0.7692308 0.2820513
#> 6: 1.50827 0.7692308 0.2820513
#> passengers_catch22_SP_Summaries_welch_rect_centroid passengers_catch22_FC_LocalSimple_mean3_stderr
#> <num> <num>
#> 1: 0.02454369 0.4030752
#> 2: 0.02454369 0.4030752
#> 3: 0.02454369 0.4030752
#> 4: 0.02454369 0.4030752
#> 5: 0.02454369 0.4030752
#> 6: 0.02454369 0.4030752