Create Fourier Features for Seasonality

Creates pairs of Fourier (harmonic) terms sin(2 * pi * k * t / period) and cos(2 * pi * k * t / period) as new feature columns, for k = 1, ..., K harmonics per seasonal period, where t is the per-series time position. They encode seasonality as a flexible alternative to seasonal lags, in particular for long or non-integer periods and multiple seasonalities at once.

Parameters

The parameters are the parameters inherited from mlr3pipelines::PipeOpTaskPreprocSimple, as well as the following parameters:

• period :: numeric() | NULL
  Seasonal period(s), in number of observations per cycle. May be non-integer and may contain multiple periods for multiple seasonalities. If NULL (default), the period is derived from the task's frequency (task$freq).

• K :: integer()
  Number of Fourier harmonics per period. Either a single value recycled to all periods, or one value per period. Each K must satisfy 2 * K <= period. Default 1L.

References

De Livera AM, Hyndman RJ, Snyder RD (2011). “Forecasting time series with complex seasonal patterns using exponential smoothing.” Journal of the American Statistical Association, 106(496), 1513–1527.

Hyndman RJ, Khandakar Y (2008). “Automatic Time Series Forecasting: The forecast Package for R.” Journal of Statistical Software, 27(3), 1–22. doi:10.18637/jss.v027.i03 .

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFcstFourier

Methods

Public methods

• PipeOpFcstFourier$new()

• PipeOpFcstFourier$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

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PipeOpFcstFourier$new()

Initializes a new instance of this Class.

Usage

PipeOpFcstFourier$new(id = "fcst.fourier", param_vals = list())

Arguments

id

(character(1))
Identifier of resulting object, default "fcst.fourier".

param_vals

(named list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

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PipeOpFcstFourier$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpFcstFourier$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3pipelines)
task = tsk("airpassengers")
po = po("fcst.fourier", period = 12, K = 3L)
new_task = po$train(list(task))[[1L]]
new_task$head()
#>    passengers     S1_12      C1_12      S2_12 C2_12 S3_12 C3_12
#>         <num>     <num>      <num>      <num> <num> <num> <num>
#> 1:        112 0.5000000  0.8660254  0.8660254   0.5     1     0
#> 2:        118 0.8660254  0.5000000  0.8660254  -0.5     0    -1
#> 3:        132 1.0000000  0.0000000  0.0000000  -1.0    -1     0
#> 4:        129 0.8660254 -0.5000000 -0.8660254  -0.5     0     1
#> 5:        121 0.5000000 -0.8660254 -0.8660254   0.5     1     0
#> 6:        135 0.0000000 -1.0000000  0.0000000   1.0     0    -1