tsl_smooth: Smoothing of Time Series Lists

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Smoothing of Time Series Lists

Description

Rolling-window and exponential smoothing of Time Series Lists.

Rolling-window smoothing This computes a statistic over a fixed-width window of consecutive cases and replaces each central value with the computed statistic. It is commonly used to mitigate noise in high-frequency time series.

Exponential smoothing computes each value as the weighted average of the current value and past smoothed values. This method is useful for reducing noise in time series data while preserving the overall trend.

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

Usage

tsl_smooth(tsl = NULL, window = 3, f = mean, alpha = NULL, ...)

Arguments

tsl	(required, list) Time series list. Default: NULL
window	(optional, integer) Smoothing window width, in number of cases. Default: 3
f	(optional, quoted or unquoted function name) Name of a standard or custom function to aggregate numeric vectors. Typical examples are mean, max,min, median, and quantile. Default: mean.
alpha	(required, numeric) Exponential smoothing factor in the range (0, 1]. Determines the weight of the current value relative to past values. If not NULL, the arguments window and f are ignored, and exponential smoothing is performed instead. Default: NULL
...	(optional, additional arguments) additional arguments to f.

Value

time series list

See Also

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

Examples

tsl <- tsl_simulate(n = 2)

#rolling window smoothing
tsl_smooth <- tsl_smooth(
  tsl = tsl,
  window = 5,
  f = mean
)

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

#exponential smoothing
tsl_smooth <- tsl_smooth(
  tsl = tsl,
  alpha = 0.2
)

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

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