Time Series Feature Extraction (feasts) — mlr_pipeops

Computes per-series summary features from the target variable via fabletools::features() with feature functions from the feasts package, and broadcasts them as constant columns to every row of the corresponding series. For an unkeyed task the features are broadcast to every row; for a keyed task each key contributes one feature vector.

This is the feasts (tidyverts) counterpart of PipeOpFcstTsfeats, which uses the tsfeatures package. The order column is mapped to an appropriate tsibble index (yearmonth/yearquarter/yearweek for the respective frequencies, otherwise used as-is) so that the seasonal period is inferred correctly.

Features are cached in the state at train time and reused at predict time. Predicting on a key that was not seen during training is an error.

Parameters

The parameters are the parameters inherited from mlr3pipelines::PipeOpTaskPreproc, as well as:

features :: list()
A list of feasts feature functions (e.g. feasts::feat_acf, feasts::feat_stl) or a fabletools::feature_set(). Default list(feasts::feat_acf, feasts::feat_stl).

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpFcstFeasts

Methods

Inherited methods

`PipeOpFcstFeasts$new()`

Initializes a new instance of this Class.

Usage

PipeOpFcstFeasts$new(id = "fcst.feasts", param_vals = list())

Arguments

id: (character(1))
Identifier of resulting object, default "fcst.feasts".
param_vals: (named list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

`PipeOpFcstFeasts$clone()`

The objects of this class are cloneable with this method.

Usage

PipeOpFcstFeasts$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(mlr3pipelines)
task = tsk("airpassengers")
po = po("fcst.feasts", features = list(feasts::feat_acf))
out = po$train(list(task))[[1L]]
out$head()
#>    passengers passengers_feasts_acf1 passengers_feasts_acf10 passengers_feasts_diff1_acf1 passengers_feasts_diff1_acf10 passengers_feasts_diff2_acf1 passengers_feasts_diff2_acf10
#>         <num>                  <num>                   <num>                        <num>                         <num>                        <num>                         <num>
#> 1:        112              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 2:        118              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 3:        132              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 4:        129              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 5:        121              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 6:        135              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#>    passengers_feasts_season_acf1
#>                            <num>
#> 1:                      0.760395
#> 2:                      0.760395
#> 3:                      0.760395
#> 4:                      0.760395
#> 5:                      0.760395
#> 6:                      0.760395

# select features by tag via fabletools::feature_set() (requires feasts to be attached so its
# feature registry is populated)
library(feasts)
#> Loading required package: fabletools
features = fabletools::feature_set(pkgs = "feasts", tags = "autocorrelation")
po = po("fcst.feasts", features = features)
po$train(list(task))[[1L]]$head()
#>    passengers passengers_feasts_acf1 passengers_feasts_acf10 passengers_feasts_diff1_acf1 passengers_feasts_diff1_acf10 passengers_feasts_diff2_acf1 passengers_feasts_diff2_acf10
#>         <num>                  <num>                   <num>                        <num>                         <num>                        <num>                         <num>
#> 1:        112              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 2:        118              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 3:        132              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 4:        129              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 5:        121              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#> 6:        135              0.9480473                5.670087                    0.3028553                     0.4088376                   -0.1910059                     0.2507803
#>    passengers_feasts_season_acf1 passengers_feasts_pacf5 passengers_feasts_diff1_pacf5 passengers_feasts_diff2_pacf5 passengers_feasts_season_pacf
#>                            <num>                   <num>                         <num>                         <num>                         <num>
#> 1:                      0.760395               0.9670971                      1.176436                       1.15419                    -0.1354311
#> 2:                      0.760395               0.9670971                      1.176436                       1.15419                    -0.1354311
#> 3:                      0.760395               0.9670971                      1.176436                       1.15419                    -0.1354311
#> 4:                      0.760395               0.9670971                      1.176436                       1.15419                    -0.1354311
#> 5:                      0.760395               0.9670971                      1.176436                       1.15419                    -0.1354311
#> 6:                      0.760395               0.9670971                      1.176436                       1.15419                    -0.1354311