tsl_transform: Transform Values in Time Series Lists

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Transform Values in Time Series Lists

Description

Function for time series transformations without changes in data dimensions. Generally, functions introduced via the argument f should not change the dimensions of the output time series list. See tsl_resample() and tsl_aggregate() for transformations requiring changes in time series dimensions.

This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.

Usage

tsl_transform(tsl = NULL, f = NULL, ...)

Arguments

tsl	(required, list) Time series list. Default: NULL
f	(required, transformation function) name of a function taking a matrix as input. Currently, the following options are implemented, but any other function taking a matrix as input (for example, scale()) should work as well: f_proportion: proportion computed by row. f_percent: percentage computed by row. f_hellinger: Hellinger transformation computed by row f_scale_local: Local centering and/or scaling based on the variable mean and standard deviation in each time series within tsl. f_scale_global: Global centering and/or scaling based on the variable mean and standard deviation across all time series within tsl. f_smooth: Time series smoothing with a user defined rolling window. f_detrend_difference: Differencing detrending of time series via diff(). f_detrend_linear: Detrending of seasonal time series via linear modeling. f_detrend_gam: Detrending of seasonal time series via Generalized Additive Models.
...	(optional, additional arguments of f) Optional arguments for the transformation function.

Value

time series list

See Also

Other tsl_processing: tsl_aggregate(), tsl_resample(), tsl_smooth(), tsl_stats()

Examples

#two time series
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
) |>
  tsl_subset(
    names = c("Spain", "Sweden"),
    colnames = c("rainfall", "temperature")
  )

if(interactive()){
  tsl_plot(
    tsl = tsl
  )
}

#centering and scaling
#-----------------------------------------
#same mean and standard deviation are used to scale each variable across all time series
tsl_scale <- tsl_transform(
  tsl = tsl,
  f = f_scale_local
)

if(interactive()){
  tsl_plot(
    tsl = tsl_scale,
    guide_columns = 3
  )
}


#rescaling to a new range
#-----------------------------------------

#rescale between -100 and 100
tsl_rescaled <- tsl_transform(
  tsl = tsl,
  f = f_rescale_local,
  new_min = -100,
  new_max = 100
)

#old range
sapply(X = tsl, FUN = range)

#new range
sapply(X = tsl_rescaled, FUN = range)



#numeric transformations
#-----------------------------------------
#eemian pollen counts
tsl <- tsl_initialize(
  x = distantia::eemian_pollen,
  name_column = "name",
  time_column = "time"
)

if(interactive()){
  tsl_plot(
    tsl = tsl
  )
}

#percentages
tsl_percentage <- tsl_transform(
  tsl = tsl,
  f = f_percent
)

if(interactive()){
  tsl_plot(
    tsl = tsl_percentage
  )
}

#hellinger transformation
tsl_hellinger <- tsl_transform(
  tsl = tsl,
  f = f_hellinger
)

if(interactive()){
  tsl_plot(
    tsl = tsl_hellinger
  )
}

distantia documentation built on April 4, 2025, 5:42 a.m.